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Research Article

Global tourist flows under the Belt and Road Initiative: A complex network analysis

Roles Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft

Affiliation School of International Business, Southwestern University of Finance and Economics, Chengdu, Sichuan, China

Roles Conceptualization, Supervision, Writing – review & editing

Roles Conceptualization, Methodology, Supervision, Visualization, Writing – review & editing

* E-mail: [email protected]

• Oleksandr Shymanskyi, 
• Jue Wang, 

• Published: August 16, 2022
• https://doi.org/10.1371/journal.pone.0272964
• Reader Comments

This study applies complex network analysis to examine global tourist flows network in the context of Belt and Road Initiative (BRI). Using tourist flows data between 221 countries/regions over 1995–2018, we investigate the nature and development patterns of structural properties of global network as well as factors influencing its formation. The descriptive analysis indicates that global tourist network was a sparse network with small world network characteristics. According to centrality characteristics, China showed the most influence in the BRI group, while Germany and the United States possessed key roles among non-BRI countries/regions. Exploratory analysis demonstrated significant influence of gravity variables in global, BRI and non-BRI tourist networks. This research advances existing tourism theory and provides practical implications for policymakers.

Citation: Shymanskyi O, Wang J, Pu Y (2022) Global tourist flows under the Belt and Road Initiative: A complex network analysis. PLoS ONE 17(8): e0272964. https://doi.org/10.1371/journal.pone.0272964

Editor: Dragan Pamucar, University of Belgrade Faculty of Organisational Sciences: Univerzitet u Beogradu Fakultet organizacionih nauka, SERBIA

Received: January 4, 2022; Accepted: July 31, 2022; Published: August 16, 2022

Copyright: © 2022 Shymanskyi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data cannot be shared publicly because it is not available for free. The data used in this study can be purchased from the UNWTO e-library ( https://www.e-unwto.org/action/doSearch?ConceptID=2476&target=topic ) for each country individually. The authors received no special access privileges others would not have.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

International tourism is a promising sector of trade in services that was demonstrating one of the fastest growth rates. The number of international arrivals reached 1,460 million in 2019. Although it was a smaller 4% increase compared to 7% in 2017 and 6% in 2018, the industry saw a tenth consecutive year of sustained growth in tourist arrivals. Similarly, tourism spending continued increasing, especially among the world’s top ten spenders, despite global economic slowdown. In particular, export revenues from tourism reached US $1.7 billion with China, the USA, Germany, the United Kingdom and France being among the top five world spenders [ 1 ].

Tourism industry was also placed in an important strategic position by the Belt and Road Initiative, launched by China in the end of 2013. Having cultural and historical resources to promote mutual exchanges between the countries/regions along the BRI, international tourism was expected to revive the ancient Silk Road route [ 2 ]. Since the BRI started, it gained support from over 140 countries/regions [ 3 ], some of which jointly hosted ‘Year of Tourism’ and already set up various tourism cooperation mechanisms [ 4 ]. Along with that, the Chinese government was putting efforts to facilitate tourist flows between the BRI countries/regions through visa relaxation, risk management and direct flights [ 5 ].

Indeed, international tourist flows for long time was in the focus of tourism industry players and researchers, who typically examined them through the analysis of tourist arrivals and tourism revenues in econometric methods [ 5 – 9 ]. Despite that, structural properties of global tourist flows received less attention in tourism research [ 10 ]. With respect to an overly increasing number of the BRI members, understanding their flow patterns in relation to other countries/regions could provide valuable insights. Additionally, identification of major determinants of global tourist flows network as well as tourism networks of BRI and non-BRI countries/regions should increase tourism management efficiency and marketing competitiveness in destinations.

A complex network analysis is a mathematic and graph theory based approach that concerns itself with structural analysis and visualization of flows, movements and relationships between network actors [ 11 ]. Having originated from social fields, the application of network analysis became interdisciplinary as scholars started experimenting with investigating various types of relationships [ 12 ]. Actors who establish relations could be individuals, organizations and other entities, whereas goods, services and information among others might represent different types of relationships [ 13 ].

In tourism literature, the network analysis approach was employed to measure tourist flows in particular destinations [ 14 – 16 ] and between countries [ 10 , 17 ]. For example, Shih [ 18 ] applied this methodology to analyze drive tourism destinations focusing on node ties, while Leung, Wang [ 14 ] investigated movement patterns of overseas tourists in Beijing during the Olympics. By utilizing complex network, Guo, Zhang [ 19 ] examined the fluctuation patterns of monthly inbound tourist flows in China sharing valuable insights on the nature of tourism demand. Shao, Huang [ 10 ] applied this approach to illustrate the evolution of international tourist flows over 1995–2018 focusing, in particular, on properties of tourist flows network as well as the roles and functions of countries/regions within it.

Similar to network analysis, the BRI research was receiving increasing attention in tourism literature recently (see S1 Table ). This is unsurprising since the B&R initiative views tourism as one of its main components that represents mutual exchanges and friendly cooperation aimed at fostering people-to-people bonds. For instance, Ahmad, Draz [ 20 ] investigated the effects of tourism on environmental situation in Chinese key BRI provinces. Deng and Hu [ 2 ] focused on the spillover effects of Chinese outbound tourism to 55 BRI countries. Huang, Han [ 5 ] demonstrated that the BRI policy had positive effect on China’s inbound tourism, while Li, Shi [ 4 ] found positive influence of BRI policy on inbound tourism and tourism revenues of participating countries. Liu and Suk [ 21 ] examined sustainable tourism development strategy between China and Azerbaijan within the BRI, whereas Li, Tavitiyaman [ 22 ] defined factors influencing tourist arrivals from Russia and Mongolia in China. Chen, Cui [ 23 ] studied the relationship between economic growth and tourism revenue along the BRI.

While tourism studies related to the BRI typically use econometric techniques, to the best of our knowledge, the network analysis was not applied to examine tourist flows across the BRI countries. Further, a considerable number of tourism publications that uses this methodology commonly focuses on structural characteristics (e.g. centrality characteristics) of networks [ 10 , 11 , 16 ], which are purely of descriptive nature. Agreeing with Liu, Huang [ 15 ], we state that descriptive studies are not able to reveal the underlying mechanisms of network formation. In addition, a number of investigated tourism networks are static [ 17 , 24 ] and do not provide enough information to examine how structural characteristics evolved over time. Although more sophisticated methods of network analysis such as dynamic networks [ 10 , 25 ], the Quadratic Assignment Procedure (QAP) regression [ 15 , 26 ] and agent-based network model [ 27 ] are being used, their overall number is relatively low. In addition, gravity variables that emphasize the impact of various dimensions of distance [ 28 – 31 ], commonly used in tourism demand modeling [ 32 ], were not investigated in network setting.

Based on the mentioned above, our research employs the complex network analysis methodology to analyze tourism data of 221 countries/regions over 1995–2018 in the context of BRI. Specifically, descriptive part examines the nature of global tourist flows network and centrality properties in the most influential BRI and non-BRI countries/regions, whereas exploratory part, using distance related gravity variables [ 28 , 32 ], applies the QAP approach to analyze and compare the influencing factors in global, BRI and non-BRI tourist networks. As such, current study contributes to literature in a number of ways. Firstly, we describe the network centrality characteristics focusing on major countries/regions that belong to BRI and non-BRI groups with further comparative analysis. Secondly, our research employs longitudinal data to demonstrate the evolution of travel patterns of visitors from BRI and non-BRI countries. Thirdly, this study applies a more sophisticated exploratory analysis that is based on gravity theory to investigate underlying factors of network formation in global, BRI and non-BRI tourism networks.

The remainder of this paper is organized as follows. Section 2 describes data sources and network analysis methodology. Section 3 discusses the results obtained from descriptive and exploratory parts of network analysis. Section 4 sums up the results with subsequent theoretical and practical implications as well as limitations and future research directions.

Materials and methods

Data sources and preparation, dependent variable..

The data on tourist flows between countries/regions were accessed from United Nations World Tourism Organization [ 33 ]. It provides tourism data from eight information sources that are based on countries’ methods to report their tourism statistics. Since countries employ varying reporting techniques, we had to select the information source individually for every country that would provide the largest amount of tourism data. Although the tourism data were available from 1995 to 2019 year, the last year was excluded due to a large share of missing data that could have negatively influenced the interpretation of network structural features. As such, the study period of current research is from 1995 to 2018 years.

Independent variables.

The data on independent variables were collected from the gravity database available at CEPII [ 34 ] website. They include physical distance between most populated city of each country/region, common language , contiguity , common religion that adds up products of shares of Catholics, Protestants and Muslims in a particular pair of countries/regions, gross domestic product (in current thousands US dollars) used for calculating economic distance expressed as absolute difference between GDPs of certain countries/regions.

Belt and Road counties.

In this paper, the information on countries/regions participating in the B&R Initiative (see S2 Table ) was kindly provided by Green Belt and Road Initiative Center [ 3 ]. Following Kang, Peng [ 35 ], we regarded Hong Kong (China) and Macau (China) as non-BRI members, while Taiwan (China) was labeled as ‘other’ country/region within non-BRI group owing to its economic development and international classification [ 33 ].

The methodology of network analysis

In this research, we used network analysis to examine global tourist flows. Graphically, simple representation of our network is illustrated in Fig 1 , in which importance of international tourist flows is reflected through the thickness of ties. Using tourism data of 221 countries/regions over 1995–2018, we analyze network indicators that are discussed below.

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https://doi.org/10.1371/journal.pone.0272964.g001

Number of nodes and ties within network.

The node number represents the quantity of countries/regions our network contains in a particular year and the number of ties demonstrates how many outbound or inbound tourist flows occur between countries/regions in a certain year.

Network density.

Transitivity.

s i (k i − 1) is the normalization factor that considers the weight of each tie; w ij and w ih denote the weights of ties; and a ij , a ih , a jh imply nodes of the adjacency matrix.

Reciprocity.

Average shortest path length (APL).

Betweenness centrality.

Degree centrality.

Strength centrality.

The quadratic assignment procedure

In this study, tourist flows between countries/regions represent dependent variable, a weighted matrix ranging from 215 × 215 countries/regions in 1995 to 221 × 221 countries/regions in 2018. In this matrices, each cell (row i , column j ) contains a number of tourists from origin country/region i to destination country/region j . Further, a number of matrices of independent variables such as physical distance, common language, contiguity, common religion and economic distance are selected in this study. We expect that all variables expect physical distance will have positive influence on tourist flows.

Results and discussion

The structural features of global tourist flow network.

Analysis of the network characteristics allows us to understand better how tourist flows were evolving in global network and what development patterns emerged. Table 1 demonstrates structural features of 221 countries/regions within global tourism network over 1995–2018 years.

https://doi.org/10.1371/journal.pone.0272964.t001

The number of nodes, in general, was constantly growing from 215 in 1995 to 221 in 2018. Considering the number of ties, we can observe positive trend up to 2016 interrupted by slight decline. We believe that the number of tourist flows (ties) between countries/regions, in fact, was consistently increasing until the end of the study period. The same logic applies to three following properties–density, transitivity and reciprocity, which demonstrated positive trends despite lower values in the end of the study period. With that in mind, density indicated increased interaction among countries/regions in tourist flows network possibly due to globalization [ 43 ] and tourism related government policies such as the BRI [ 4 ]. At the same time, relatively low value of density implied that global tourist flows network was a sparse network and exhibited scale-free power-law distribution [ 44 ]. Further, the values of transitivity reported that countries/regions had tendency to cluster together within network forming communities that were highly connected, and network reciprocity reported growing mutual cooperation between countries/regions. Finally yet importantly, the APL was continuously declining over 1995–2018 meaning that the effectiveness of tourist flows improved owing to shorter distance between countries/regions.

The nature of global tourism network

We additionally examined the transitivity (clustering coefficients) and APL of tourist flows network, values of which were subsequently compared with those of 1000 random networks that had the same number of nodes and ties to test the properties of ‘small world’ network [ 45 ]. This type of network is characterized by short mean distance between pairs of network nodes in comparison to the total number of nodes. Fig 2 illustrates that our tourism network had higher values of the transitivity (clustering coefficients) compared to those of the corresponding random network implying significant effect of tourist flows between countries/regions. Considering the APL, we can observe that their values were lower (shorter) in the real network, except for the first year. This infers that since 1996 our tourism network was demonstrating properties of small world network that are distinguished by two major characteristics: a short APL and high transitivity (clustering coefficients). This means that the actors in our network needed a rather small number of connections to reach each other [ 45 ].

The red line represents transitivity (clustering coefficients) and average path length (APL) of real network, whereas the blue shaded area shows the maximum and minimum values of these indices obtained from 1000 random networks.

https://doi.org/10.1371/journal.pone.0272964.g002

The structural features of countries/regions within the global tourist flow network

Mediating roles of countries/regions in global tourist flows network..

Betweenness centrality shares insights regarding countries/regions that exercise power through bridging positions within the global tourism network. Table 2 reports the betweenness centralities of top 15 BRI non-BRI countries/regions for the selected years demonstrating steady downward trajectory for the majority of countries/regions. This implies that the intermediary roles of countries/regions was diminishing throughout the entire study period.

https://doi.org/10.1371/journal.pone.0272964.t002

Fig 3 illustrates the development trends of betweenness centrality for top five BRI and non-BRI countries/regions with some degree of variation over 1995–2000. In addition to clear division between both groups, all non-BRI countries/regions were located above BRI. More specifically, the United States and Canada had the highest values ranking first and second during the majority of the study period. A considerable gap existed between them and the other countries/regions (both BRI and non-BRI), whose positions were not far from each other. The remaining non-BRI countries/regions were represented by Belgium, Australia and Japan with respective third, fourth and fifth rankings. Considering the BRI members, China exceeded other BRI countries competing with Japan for fifth place over the last years, however, ranked sixth by the end of the study period. Further, New Zealand was placed right between China and three other BRI countries over 2013–2018 ranking seventh. The rest of the BRI countries/regions including Korea (Republic of), South Africa and Turkey all were having rather similar values of betweenness centrality in the last years of the study period and ranked eighth, ninth and tenth, respectively.

Countries/regions were selected based on the highest values of 2018 year; BRI marked with ‘*’.

https://doi.org/10.1371/journal.pone.0272964.g003

The connectedness of countries/regions in global tourist flows network.

Degree centrality of countries/regions was examined to describe their connectedness with each other. In Table 2 , we can see that the number of connections, in general, was increasing among BRI and non-BRI groups, however, slightly declined in the end of the study period (possibly due to slightly larger number of missing data).

Focusing on top 5 BRI and non-BRI countries/regions, Fig 4 demonstrates that non-BRI countries/regions remained the same as those in Fig 3 , whereas in BRI group South Africa was replaced by Poland. The major difference with betweenness centrality is that all countries/regions (regardless of group division) had upward trend, which slightly declined in 2017 and 2018. Further, the countries’/regions’ rankings had slight changes: despite the fact that the United States, Canada, Belgium and Australia held first four rankings, now China ranked fifth having surpassed Japan (ranked sixth). The remaining four BRI countries, same as in betweenness centrality graph, were located below their non-BRI counterparts and China. Interestingly, a significant boost in degree centrality of the United States, Australia, Canada and Poland can be seen in the early years of the study period (1995–2000), while Turkey and Japan experienced rapid growth during the last years of the study period (2012–2014).

Countries are selected based on the highest values of 2018 years; BRI marked with ‘*’.

https://doi.org/10.1371/journal.pone.0272964.g004

The impact of countries/regions in global tourist flows network.

Table 2 provides the information on strength centrality of countries/regions that shows their roles on both outbound and inbound tourism markets. Opposite to betweenness and rather similar to degree centrality, the values of strength centrality were showing steady growth for the majority of countries/regions.

Five BRI and non-BRI countries/regions with the highest strength centrality values are demonstrated in Fig 5 . As can be seen, this time the majority of countries/regions again had upward trend throughout the entire study period, regardless of some minor declines over 2000–2003 and 2007–2009. In particular, having exceeded all other countries/regions, China was ranking first since 2002 until the end of the study period. It is worth mentioning that the gap between China, non-BRI and the remaining BRI countries/regions was increasing after China’s accelerated growth in 2009. In non-BRI group, the United States, Hong Kong (China), Germany, Italy and France had somewhat similar development patterns with respective second, third, fourth, fifth and sixth rankings. Speaking of BRI countries/regions, we can observe quite different development trends. For instance, after 1999–2002 decline, Poland dropped from third to seventh position yielding to non-BRI group, which it was trying to catch up with ever since. In the last years of the study period, Poland got closer to France and Italy. Further, regardless of decline during 2013–2016, Russian Federation did not change its position and ranked eighth in 2018. Two remaining BRI countries/regions Korea (Republic of) and Thailand had almost the same growth trajectory repeatedly exchanging their rankings throughout the entire study period. As of 2018, Thailand ranked ninth, whereas Korea (Republic of) was tenth.

In 10 thousand people, countries/regions are selected based on the highest values of 2018 years; BRI marked with ‘*’.

https://doi.org/10.1371/journal.pone.0272964.g005

The impact of countries/regions on outbound tourism.

We investigated out-strength centrality to define what countries/regions had significant impact in outbound tourism market. Table 3 reports out-strength of 25 BRI and non-BRI countries/regions for selected years that are of major importance for outbound tourism. In 2018, both groups accounted for 72% of the sum of out-strength centrality in the 221 countries/regions. Specifically, the share of 25 BRI made 22.3%, whereas 25 non-BRI contributed to 49.7%, respectively.

https://doi.org/10.1371/journal.pone.0272964.t003

Fig 6 provides the visual representation of the out-strength centrality of top 5 BRI and non-BRI countries/regions. In 2018, China had the highest out-strength compared to other countries/regions, which is not surprising, considering country’s unprecedented economic growth and overly increasing role in outbound tourism market [ 46 ]. Indeed, China’s out-strength values saw dramatic increase from 4794003 in 1995 to 145796484 in 2018, which is more than 30 times during 24 years. Over 1995–2007, a number of countries/regions such as Saudi Arabia, Ukraine, Korea (Republic of), Russian Federation, and France were surpassed by China. Throughout the entire study period, China’s out-strength just slightly declined in 2008. Starting from 2009, it was growing at an even faster pace exceeding the United Kingdom in 2010, the United States in 2012, Hong Kong (China) in 2013 and Germany in 2014, ranking first and becoming a major generating country of outbound tourist flows until the end of the study period.

In 10 thousand people; countries/regions are selected based on the highest values of 2018 years; BRI marked with ‘*’.

https://doi.org/10.1371/journal.pone.0272964.g006

During 1995–2013, Germany was the largest source of outbound tourists and after yielding leadership to China in 2014 ranked second, being followed by the USA. Despite both Germany and the USA experienced declines in out-strength (1999–2003 and 2007–2009 in Germany, 2000–2003 and 2006–2009 in the USA), downturns in Germany were much more severe (see 2001 and 2009), whereas the USA was able to recover relatively faster and had increased its growth pace starting from 2013 and almost caught up with Germany in 2018. Hong Kong (China), in general, was growing steadily without serious effects caused by declines. Over 2003–2014, it surpassed the USA, however, quickly yielded after that period and was ranking fourth until 2018. Next, the United Kingdom and France both demonstrated rather steady growth over 1995–2018, whereas the former had somewhat steeper upward/downward trajectories.

Russian Federation was demonstrating steady growth over 1995–2013 with two slight declines in 1999 and 2009, respectively. After that, it experienced a sharp downturn during 2013–2016 due to a number of reasons that might be explained by oil price decrease, tense political relations with Ukraine [ 47 ] and subsequent sanctions imposed by Western countries [ 48 ]. Although the out-strength value was recovering since, Russian Federation did not catch up with the value of 2013 year ranking sixth by the end of the study period. By contrast, Korea (Republic of), was steadily growing over the study period without negative consequences from declines of 1998 and 2009. In 2016, it exceeded Russian Federation, but in spite of this, two years later was surpassed by the latter and ranked seventh. Over 1995–2009, Ukraine experienced smooth upward and downwards trends and had been growing steadily since 2009. This is quite surprising, considering that in the end of 2013 a series of protests took place in Ukraine, which were followed by government overthrow in 2014 and military conflict with the most severe phase during 2014–2016. None of these factors, seemingly, impeded the growth of out-strength of Ukraine, which ranked ninth in 2018. Lastly, Saudi Arabia was continuously growing during the entire study period, especially in 2015 when country’s outbound tourism market rose almost twice compared to 2014. Regardless of this, the values of Saudi Arabia were lower in relation to other countries/regions making it rank tenth in 2018.

By comparing two groups of top 5 BRI and non-BRI countries/regions, we can see that China was the only BRI country/region that had higher out-strength value than non-BRI countries over relatively long time. Another case of BRI country/region surpassing non-BRI happened in 2013, when Russian Federation exceeded France, however it quickly conceded in the next year (due to the reasons described in the paragraph above). In our opinion, non-BRI countries/regions showed higher values than the majority of BRI countries/regions since all of them were enjoying high-economic development level for a relatively long time, whereas most of the BRI countries/regions are regarded as developing or became developed rather recently. In addition, the out-strength values of the BRI countries/regions, except for China, are normally more concentrated next to each other, especially in the first and last years of the study period.

The impact of countries/regions on inbound tourism.

In-strength centrality was analyzed to understand which countries had great influence in the inbound tourism network. Table 4 provides information on the in-strength centralities for selected years of top 25 BRI and non-BRI countries for selected years. As of 2018, both groups contributed 71.3% out of sum of in-strength centrality in the 221 countries/regions. More precisely, BRI members accounted for 34.6%, whereas non-BRI countries/regions had 36.7% share.

https://doi.org/10.1371/journal.pone.0272964.t004

In Fig 7 , we can observe that the variation of in-strength centrality is lower compared to that of out-strength ( Fig 6 ). Regarding the countries/regions, China again demonstrated dramatic growth, especially in the early years (1995–2002) of the study period. In 2001, it already ranked first holding leadership until the end of the study period, regardless of two slight declines in 2003 and during 2007–2009. The in-strength value demonstrates that China been of great interest to inbound tourists since the very beginning, whereas China’s out-strength shows that it became a major source of outbound tourists after 2009. Poland, another BRI member, experienced a series of downward/upward trends that had been decreasing/increasing the country’s ranking throughout the study period. After the second slight downturn over 2007–2009, it had been constantly growing since, having obtained the second ranking, and almost reached its highest in-strength value, which Poland once had prior to the first sharp decline (1999–2002).

https://doi.org/10.1371/journal.pone.0272964.g007

As to the other leading countries/regions, Italy ranked third in 2018, which is the highest position among non-BRI countries/regions. The country’s growth can be described, in general, as positive and steady with several minor declines that were always recovered in each following year. Italy would often yield/regain its ranking mainly competing with countries/regions such as France, the United States and Poland. In 2016, it exceeded the United States (leaving it fourth ranking), and almost caught up with Poland during the last years. Quite interestingly, Hong Kong (China) had the lowest in-degree value among non-BRI countries, however, was able to surpass Spain and France due to a rapid growth over 2009–2014 and eventually ranked fifth in 2018. France used to have relatively high ranking during 1999–2005, but due to lack of significant growth in the following years, it ranked sixth. In a similar fashion, Spain, regardless of sharp declines, was unable to catch up with other countries/regions and ranked seventh.

The remaining Turkey, Thailand and Greece are all BRI members that had somewhat similar development patterns during 1995–2006. In the following years, Turkey accelerated growth, which declined over 2014–2016 yielding to Thailand, but then regained its position back and ranked eighth in 2018. In its turn, Thailand, whose values were rather similar to those of Greece, broke away obtaining ninth ranking. Although Greece had positive trend, it was not enough to catch up and exceed other BRI countries/regions and, eventually, resulted in tenth ranking.

Comparing the in-strength of BRI and non-BRI groups, China again showed leadership owing to outstanding growth and now was accompanied by Poland. Next, the majority of countries/regions in both groups did not vary much (except China) and had somewhat similar trajectories. Although this time two BRI countries/regions had the highest rankings, Poland did not break away from other non-BRI countries and most of the BRI countries were again positioned below the non-BRI group.

The underlying mechanism of the global tourism network

In our analysis, matrix of tourist flows was regressed by several matrices of independent variables that included physical distance, common language and religion, contiguity and economic distances for each year using the QAP method. Before conducting analysis, the matrices of tourist flows, physical and economic distances were transformed to logarithmic form. Table 5 presents the regression output over 1995–2018 in global, BRI and non-BRI tourist network.

https://doi.org/10.1371/journal.pone.0272964.t005

From the adjusted R 2 values, we can see that on average 87.9% of variance in the tourism network can be explained by physical distance, common language, contiguity, common religion and economic distance. Despite slight decline from 89.8% in 1995 to 87.2% in 2018, the value of R 2 could explain the majority of variance in every year.

Speaking of independent variables, physical distance represented major obstacle for flows in tourism networks, which is also consistent with results obtained from gravity tourism demand models [ 28 ]. Judging by its coefficients, we can observe that physical distance had greater adverse effect on tourist flows in BRI countries/regions compared to non-BRI. Common language that reflects cultural similarity [ 29 ] exhibited significant positive influence and turned out to be more important for BRI tourists. Contiguity, used to measure geographic proximity [ 30 ], reported significant positive influence with the highest coefficients compared to those of other variables and had much greater impact on the BRI group. Common religion, which is another proxy of cultural similarity in gravity models [ 31 ], influenced tourist flows at 5% significance level in BRI and non-BRI samples with stronger impact on the latter one. Unexpectedly, economic distance that represents difference in economic development [ 49 ] between countries/regions demonstrated positive influence meaning that tourists were willing to travel to countries/regions, whose economic development was different from that of their home countries. This factor was more important for travelers from non-BRI group.

Conclusions

Discussion and conclusions.

In this study, we investigated global tourist flows of 221 countries/regions over 1995–2018 using complex network analysis. The main goals of this research were to conduct descriptive analysis examining the evolution of structural properties of tourism network in the context of B&R Initiative and apply exploratory analysis to define on annual basis the underlying mechanisms of global tourism network as well as BRI and non-BRI tourism networks.

At macro level, the descriptive part indicated that global tourism network was a sparse network and exhibited small world properties. Having much fewer links than possible maximum, our network exhibits scale-free power-law distribution [ 44 ], whereas its actors are able to reach each other through a relatively small number of connections [ 45 ]. At the same time, growing values of network density over time might be related to the globalization trend [ 43 ] and government policies (such as the BRI) aimed to strengthen tourism exchanges [ 4 ]. These assumptions can be justified by declining betweenness centralities and increasing of particular countries/regions in global tourism network that show diminishing power of their intermediary roles along with growing degree and strength centralities that imply increasing connectivity between them.

At micro level, the fluctuations of out- and in-strength centralities in particular countries/regions could be related to events including severe acute respiratory disease (2003), global financial crisis (2008), the influenza A (H1N1) epidemic (2009), however their magnitude was rather smaller compared to regional crises. As an example, political instability in Ukraine (lasting since 2013) [ 47 ], following economic sanctions imposed on Russia (since 2014) [ 48 ], coup d’état in Thailand (2014) [ 50 ] and Russia’s temporary travel ban to Turkey (2015) had far more severe effects on tourism of the respective countries/regions affecting their out- and in-strength centralities.

Tourism literature suggests that factors influencing tourist flows might be related to tourists’ income [ 9 ], overall destination quality [ 28 ], demographic structure [ 51 ], government policies [ 52 ] and tourism attractiveness [ 53 ] among others. For instance, following unprecedented economic growth, Chinese inbound and outbound tourism saw considerable increase [ 46 , 54 ] further enhanced by more recent Belt and Road Initiative [ 2 , 5 ]. In a similar way, Germany and the USA have high out-strength centralities owing to developed economies and large populations. Unimpeded by political instability, Ukraine’s out-strength continued steady growth and was facilitated by visa liberalization with the European Union in 2017, which particularly was contributing to the in-strength centrality of Poland. A number of countries/regions with high in-strength were regarded attractive from tourists’ perspective as classic beach tourism destinations (e.g. Italy, Thailand) and/or having rich cultural and historical heritage (France).

Out-strength values showed that important BRI countries/regions in outbound tourism market included China, Russian Federation, Korea (Republic of), Ukraine and Saudi Arabia, while the non-BRI group was represented by Germany, Hong Kong (China), United Kingdom and France. There was a clear division between both groups, in which non-BRI was above BRI (except China) implying greater role of the former one in outbound tourism. As to inbound tourism market, the in-strength values reported that important BRI members were China, Poland, Greece, Thailand and Turkey, while non-BRI countries/regions included Italy, the United States, Hong Kong (China), France and Spain. In BRI group, China and Poland were followed by five non-BRI and the remaining three BRI countries/regions. Although the role of BRI countries/regions as attractors of inbound tourists was higher compared to the outbound tourism, the non-BRI group, in general, had more power.

In explanatory part, a more sophisticated analysis was conducted to verify whether gravity theory, a widely used framework in tourism modeling [ 32 ] that emphasizes the role of various dimensions of distance, would also have significant impact on tourist flows in network setting. To achieve this, the QAP methodology tested the impact of traditional gravity variables such as physical distance [ 28 ], common language [ 29 ], contiguity [ 30 ], common religion [ 31 ] and economic distance [ 49 ] in global, BRI and non-BRI tourism networks. Our results demonstrated that physical distance represented a major obstacle for the international tourism, while common language, contiguity and common religion diminished travel barriers between countries/regions increasing tourist flows. Unexpectedly, our models revealed that tourist flows rose with a greater economic distance between origin and destination countries/regions implying that different level of economic development plays role of facilitator in tourism networks. Factors such as common language and contiguity were more important in the BRI tourism network, while physical and economic distances as well as common religion played greater role in the non-BRI tourism network.

Theoretical and managerial implications

Our study has several theoretical contributions. Firstly, we examine the nature of global tourist network and its development patterns in relation to major concepts of network theory. Whereas tourism studies related to BRI typically apply econometric modeling, our research is the first to apply complex network analysis to investigate the evolution of centrality characteristics of the BRI members and compare them to non-BRI group. In this regard, the tourism networks are investigated over a relatively long period, while the majority of publications with network analysis typically focuses on static networks [ 16 , 17 ]. Finally, acknowledging that descriptive methodology cannot reveal the underlying mechanisms of network formation [ 15 ], our research employs a more sophisticated QAP analysis and verifies that gravity variables have significant influence on tourist flows in global, BRI and non-BRI networks.

The obtained findings imply a number of practical implications. Tourism in destination countries/regions should be developed considering existing and prospecting relationships with origin countries/regions. This means that future policies could prioritize important countries/regions while designing tourism strategies, developing tourism programs and constructing tourism facilities. At the same time, it is necessary to remember about tourists’ preferences such as inclination to visit neighboring countries/regions rather than distant ones, desire to travel to places with common language and religion as well as willingness to have travel experiences in economically different countries/regions. Lastly, policymakers should develop strategies by prioritizing the importance of each factor on tourists’ behavior in overall, BRI and non-BRI networks.

Limitations and future research

In this study, the quality of tourism data led to the exclusion of 2019 year and influenced the development trends of structural properties in 2017 and 2018. By selecting tourism classifications with the largest data, our study could not precisely examine tourists’ behavior. In addition, a considerable number of countries/regions provide data in which tourist flows to a rather limited number of countries/destinations is available.

Future research might consider examining other network properties such as modularity, dyads, roles, page ranking and others. Community analysis can shed some light on how tourist flows between countries/regions in the network were evolving. Using the QAP, a number of political, social and weather factors could be further analyzed.

Supporting information

S1 table. summary of tourism studies related to the bri..

https://doi.org/10.1371/journal.pone.0272964.s001

S2 Table. List of the BRI countries/regions by the end of 2018.

https://doi.org/10.1371/journal.pone.0272964.s002

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• Published: 17 May 2024

Understanding attractions’ connection patterns based on intra-destination tourist mobility: A network motif approach

• Ding Ding 1 , 2 ,
• Yunhao Zheng 1 , 2 ,
• Yi Zhang 1 , 2 &
• Yu Liu   ORCID: orcid.org/0000-0002-0016-2902 1 , 2  

Humanities and Social Sciences Communications volume  11 , Article number:  636 ( 2024 ) Cite this article

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• Business and management
• Complex networks
• Science, technology and society

Tourist movement patterns among attractions are complex and variable, and understanding such patterns can help manage tourist destinations more effectively. However, previous studies on tourist movement utilising complex networks have not explored the network motif approach comprehensively. Therefore, we adopted a network motif approach using social media data to extract and analyse motifs in a city network. This study analyses the attractions corresponding to the nodes in each motif, revealing the connection patterns between these attractions. We also discuss motifs between attractions with different types and titles. Popular attractions play a significant role in a local network while other attractions serve distinct functions within the network. This study’s findings enhance the significance of network motifs in examining tourist movement and deepen the understanding of recurring movement patterns between attractions. Moreover, they assist managers in developing policy tools for intelligent tourism destination marketing and planning that cater to tourists’ needs.

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Introduction.

Tourism has emerged as a leading economic activity worldwide, surpassing certain traditional industries and serving as a pivotal catalyst for international and regional economic growth. Specifically, urban tourism constitutes the cornerstone of contemporary tourism and has reached an advanced stage of competitive growth (Cárdenas-García et al., 2024 ; W. Su et al., 2003 ). Tourism has generated business prospects for numerous companies within renowned tourist cities such as Paris, New York, and Tokyo, simultaneously creating many employment opportunities for the urban population and infusing consistent economic vibrancy into such destinations (Hassan, 2000 ). Contemporary tourists’ movements in cities are no longer bound by rigid schedules or fixed itineraries, and their temporal and spatial flexibility is highlighted by their mobility patterns. John Urry’s ‘new mobility paradigm’ focuses on such changing nature of mobility (Korstanje, 2018 ; Merriman, 2012 ; Tzanelli, 2021 ; Urry ( 2008 )).

The rapid development of information and communication technologies has led to the widespread popularisation of mobile terminal devices equipped with positioning technology. The large amount of user location data collected by these devices has significantly enhanced our understanding of tourist mobility over the past two decades (Chen et al., 2022 ; Chuang, 2023 ; Jiang & Phoong, 2023 ; Leng et al., 2021 ; Nguyen & Nguyen, 2023 ; Park et al., 2023 ; Xu et al., 2024 ). By leveraging these datasets, diverse research methods and theories have been used to investigate tourist mobility, including geographic information systems (Lau & McKercher, 2006 ), time geography (Grinberger et al., 2014 ; Xiao-Ting & Bi-Hu, 2012 ), and Markov chains (Vu et al., 2015 ; Xia et al., 2009 ). Tourism researchers have attempted to understand the essence of tourist mobility because it plays a key role in attraction marketing, event planning, and the management and design of attractions in cities. Understanding tourist mobility with a city as a single destination can help managers make refined decisions compared to tourist movement on a larger scale, such as movement between destinations. Notably, a prevalent trend involves the aggregation of individual-level mobility data into networks, which serve as a basis for analysing the topological structure of attraction systems (Vu et al., 2015 ).

Tourist mobility data correspond to a network structure in which tourist attractions are nodes and the spatial movement between two attractions represents bonds (Kang et al., 2018 ). Consequently, network analysis is a data mining technique that has been widely used to extract the connection patterns established between attractions as tourists move through a geographical space (García-Palomares et al., 2015 ; Han et al., 2018 ; Leung et al., 2012 ; Mou et al., 2020a ; Peng et al., 2016 ; Xu et al., 2022 ; Zeng, 2018 ). Understanding the network characteristics of tourist attractions has practical implications for the competitiveness, management, and planning of tourist attractions (Stienmetz & Fesenmaier, 2015 ).

Nevertheless, most current literature employing social network analysis relies on descriptive measurements to analyse tourist mobility patterns. However, this approach hampers the assessment of the reliability and validity of the identified patterns (Park & Zhong, 2022 ). This study emphasises the concept of network motifs. These motifs are characterised as recurring and statistically significant subgraphs within a larger graph. As a crucial research task in complex network theory (Ahmed et al., 2017 ), motifs reveal functional properties based on the structural traits of network systems. Examining motifs in tourism networks enhances the understanding of how destinations are connected, how tourists move between destinations and how tourism policies affect network structure and dynamics. Moreover, in comparison with the travel motifs applied in previous studies (Park & Zhong, 2022 ; Yang et al., 2017 ), network motifs provide greater insight into individual tourist mobility at the aggregation level. Consequently, motifs can be useful in identifying the most influential or central destinations in tourism networks.

In summary, this study answers the following three questions: (1) What types of motifs do attractions constitute? (2) How are motifs linked to specific attractions? (3) How do motifs relate to attraction attributes? As the first study to use network motifs to examine tourist movement in a group manner, we select Suzhou City as the case study area and adopt social media data to extract tourists’ movements, which makes it easy to connect the nodes of the network with specific attractions. This paper proceeds as follows: The Literature Review section, as its name implies, presents a review of relevant studies on tourist mobility and network motifs. The Methodology section presents the dataset used in this study and the motif discovery method that we used to analyse it. The Results section presents an analysis of the results of motif discovery. In the Discussion section, we discuss this study’s results and implications for tourism. The Conclusion section presents the conclusions of the study.

Literature review

Research on network motifs.

Network motifs are defined as patterns of interconnections occurring in complex networks at numbers that are significantly higher than those occurring in randomised networks (Milo et al., 2002 ). Motifs can characterise the dynamic and functional behaviour of a network, therefore enabling the classification of networks based on statistical analysis (Roy et al., 2023 ). Network motifs have practical implications for social relationships, protein complexes, and information infrastructures (Yu et al., 2020 ). Current methods for discovering network topics can be divided into two categories: network-centric and motif-centric approaches. Network-centric methods enumerate all subgraphs in a network, such as Mfinder (Kashtan et al., 2002 ), FanMod (Wernicke & Rasche, 2006 ), Kavosh (Kashani et al., 2009 ) and G-tries (Ribeiro & Silva, 2010 ). Conversely, approaches such as Grochow (Grochow & Kellis, 2007 ) and MODA (Omidi et al., 2009 ) are motif-centric. Based on subgraph symmetry, motif-centric methods search for a single query graph and then map frequencies rather than enumerate them.

Regarding the application of complex network science in the field of tourism, previous studies examine tourist flow networks from the perspective of inter-destination (Liu et al., 2017 ; Peng et al., 2016 ; Shih, 2006 ; Wang et al., 2020 ) and intra-destination (Gao et al., 2022 ; Hwang et al., 2006 ; Leung et al., 2012 ; Mou et al., 2020 ; Zeng, 2018 ; Zheng et al., 2021 ). Summarising these previous studies, two common types of metrics can be identified: network- and node-based metrics. Network-based indicators include density, efficiency, diameter, average shortest path, average clustering coefficient, and centralisation. Node-based metrics include degree (out and in), degree centrality (out and in), closeness centrality, and betweenness centrality. Furthermore, network structure analysis methods, such as structural holes and core-periphery analyses, have also been employed.

Although motif discovery is a crucial research area in network science, its application to tourism has been relatively limited. Several studies have been at the forefront of applying network motifs to the study of human mobility (Cao et al., 2019 ; Schneider et al., 2013 ; R. Su et al., 2020 ). In contrast to concentrating solely on subgraphs within mobility networks, researchers have introduced the concept of travel motifs, therefore expanding motifs from topological spaces to include temporal and semantic dimensions (Yang et al., 2017 ). To the best of our knowledge, this tourism network motif was first mentioned in a global tourism network study (Lozano & Gutiérrez, 2018 ). Several network motifs including transitive feedforward loops and different one and two mutual-dyads subgraphs, have been identified. Furthermore, a study conducted in South Korea introduced a network motif algorithm to examine the interconnections of travel patterns between places within the context of tourism (Park & Zhong, 2022 ), and disregarded only the spatial behaviour of local tourists. Moreover, because cell towers collect mobile sensor data, it is challenging to accurately determine the precise locations of mobile users and the tourist attractions corresponding to those specific locations. Therefore, this study employs social media data and links each tourist’s spatial behaviour to a corresponding attraction, therefore revealing the connection patterns among attractions.

Tourist mobility and network analysis

Tourist mobility encompasses tourists’ flow, movement, dispersal, and travel patterns across space and time (Hardy et al., 2020 ; Shoval et al., 2020 ). The analysis of spatial movement is a significant aspect of tourist mobility (Oppermann, 1995 ). Understanding how tourists move through time and space has important implications for infrastructure and transportation development, product development, destination planning, and planning of new attractions, as well as the management of the social, environmental, and cultural impacts of tourism (Lew & McKercher, 2006 ). The verifiability and reliability of tourist mobility studies can be improved through quantitative analysis; however, the results of quantitative analyses are highly dependent on the scales on which their studies are used (Jin et al., 2018 ; Zhang et al., 2023 ).

Some intra-destination mobility studies have conducted analyses at the individual level. For instance, Fennell ( 1996 ) examined tourist movements on the Shetland Islands using measures of space, time, perception, region, and core-periphery. Lew and McKercher ( 2006 ) used an inductive approach based on urban transportation to identify explanatory factors that influence tourist mobility within a destination. Early research favoured abstract methods based on fundamental tourist proposals. There is a general approach for modelling tourist mobility that is easy to reproduce, namely, the Markov model. Xia et al. ( 2009 ) used Markov chains to model tourist mobility as a stochastic process and calculated the probabilities of tourists’ movement patterns on an island. As a more practical and useful approach, using semi-Markov models is effective in deriving the probabilities of both tourist movement and the attractiveness of specific attractions (Xia et al., 2011 ). Furthermore, time geography is a conceptual framework used to describe and understand tourist mobility. Integrating time geography with geographic information systems tools, Grinberger et al. ( 2014 ) clustered tourists based on the time-space allocation measures of their behaviour to reveal tourists’ choices and the strategies they implemented within the constraints of time and space.

There is an increasing trend involving the aggregation of individual-level mobility data into networks as the basis for analysing the topological structure of attraction systems (Smallwood et al., 2011 ). Mobility patterns can be viewed as a network and are therefore subject to network analysis (Shih, 2006 ). Based on a core-periphery analysis of attraction networks, Zach and Gretzel ( 2011 ) analysed the structure of attraction networks and provided a strong and practical basis for technology design and tourism marketing. Leung et al. ( 2012 ) applied social network and content analyses to examine the most visited tourist attractions and main tourism movement patterns in Beijing during three distinct periods. Additionally, network methods are often used in conjunction with other analytical methods. For example, Liu et al. ( 2017 ) applied a quadratic assignment procedure to an attraction network to test the relationship between proximity and the attraction network determined by tourists’ free-choice movements. Another example is Mou et al. ( 2020a ), who integrated social network analysis with traditional quantitative methods to develop a novel research framework. Indicators such as the Annual Gini Index and Pearson correlation coefficient can also be helpful when analysing tourists’ spatiotemporal behaviour (Zheng et al., 2021 ).

Motif discovery algorithms are commonly applied to gene regulation networks, electronic circuits, and neurones (Yu et al., 2020 ). However, studies using motif discovery methods to examine study tourist mobility are limited. It is worth mentioning that there are studies that examine travel motifs (which are extended from topological spaces to temporal and semantic spaces) to ascertain tourist mobility patterns (Yang et al., 2017 ). In fact, the variation in travel mobility patterns depends not only on tourists’ different lengths of stay and the topological structures of travel mobility but also on the relative proportions of each travel mobility type (Park & Zhong, 2022 ). However, travel motifs can only reflect the movement patterns of individual tourists rather than the movement patterns at the aggregated-individual level, let alone serve as the basis for analysing the topological structure of an attraction network (Jin et al., 2018 ). To the best of our knowledge, no previous study has used network motifs to examine tourist mobility at the individual-aggregation level. Only Lozano and Gutiérrez ( 2018 ) applied the network motif analysis tool offered by UCINET 6.0 to analyse the top three global tourism flows. Therefore, this study argues that the use of network motif analysis not only fills a research gap regarding tourist mobility at the aggregation level but also provides theoretical support for planning and the design of tourist attraction networks.

Methodology

We selected Suzhou, China (Fig. 1a ) as the study area. Suzhou is located in eastern China, west of Shanghai, and has a population of five million residents. With its plentiful tourism resources, Suzhou received more than 100 million domestic visitors annually before the COVID-19 pandemic. Suzhou is well-known for its cultural and historical heritage. The most popular attractions in Suzhou are its classical gardens, which were included in the World Heritage List of the previous century. Suzhou’s ancient city attractions cover an area of 14 km 2 . In addition to these historical attractions, Suzhou has a natural landscape with lush mountains and gleaming lakes.

a The location of attractions in Suzhou b The geo-tagged microblogs in Suzhou.

Data collection and preprocessing

Social media data were primarily collected from location-based mobile phone applications. Sina Weibo, the Chinese equivalent of Twitter, is the most popular social media platform in China, with over 500 million active registered users who post 300 million microblogs daily (Kim et al., 2017 ). We used the application programming interface provided by Sina Weibo to crawl posts made in Suzhou from 12 April, 2012 to 31 October, 2016. The posts we crawled from the application programming interface contained various data about the users, including post identification (ID), user ID, post text, pictures, location information (longitude and latitude), and post time, as shown in Fig. 1b . Based on the user ID, we were also able to acquire users’ profile information while remaining compliant with user privacy regulations. User profile information includes registration location, gender, age, number of posts and fans, and ‘follows.’

However, only a portion of users were involved in tourism activities. We assumed that these tourists were not locals and that they had to return to their cities of residence after their trip. Referring to the double-filtration approach proposed by Su et al. ( 2020 ), we first filtered out local users based on the locations registered in their Weibo user profiles. In this study, the time difference between a user’s first and last post is defined as their length of stay. Referring to previous studies (Girardin et al., 2008 ; García-Palomares et al., 2015 ), we filtered out users who stayed longer than one month.

The purpose of tourism activities can be entertainment or relaxation; however, they can also be a part of official or business visits. Although official/business visits may also involve tourism activities, only visitors travelling to Suzhou of their own accord were considered tourists in our study. Therefore, during data preprocessing, we defined only users who posted microblogs within the tourist attractions shown in the Suzhou Tourism Bureau’s official list ( http://tjj.suzhou.gov.cn/ ) as tourists. Specifically, we used the coordinates recorded in the geo-tagged microblogs to determine whether a user had visited one of the attractions on the official list. After filtering out individuals based on the criteria described above, 234,049 Weibo microblogs were obtained from 54,712 tourists. Sorting these microblogs by time gave us the of tourists’ trajectories within the city. As a result, we could map these trajectories to the directional connections between attractions to establish, a network of attractions (Fig. 2 ).

Framework of the network motif analysis.

Extracting attractions’ connection patterns using network motif analysis

Just as attractions are represented in networks as nodes and flows as edges, tourist mobility patterns can also be transformed into complex networks (Schneider et al., 2013 ). Therefore, we discovered all recurrent mobility patterns related to the motifs appearing in the tourist flow network. To accomplish this, we introduced a new algorithm (Kavosh) designed to find k-size network motifs with less memory and CPU time than those required for other algorithms. The Kavosh algorithm is based on counting all k-size subgraphs of a given graph (directed or undirected). As shown in Fig. 2 , the Kavosh algorithm consists of three steps: enumeration, random network generation, and motif identification. First, the algorithm enumerates all possible mobility patterns related to the subgraphs in the original network. The Kavosh algorithm groups the isomorphic subgraphs using the NAUTY algorithm. This optimisation enhances the overall process efficiency and minimises pattern redundancy. As not all patterns bear significance, the algorithm generates a large number of random networks and compares the frequency of occurrence of these patterns in all random networks. Lastly, the significance of each pattern in the input network is calculated for motif identification. Here, some statistical measures that generate probable motifs in the original network are introduced.

This is the simplest method for estimating the significance of a motif. For a given network, we assume that G p is a representative of an isomorphism class involved in that class. Frequency is defined as the number of occurrences of G p in the input network.

This measure reflects how randomly the class occurred in the input network. For the assumed motif G p , this measure is defined as:

This measure indicates the number of random networks in which a motif, G p , occurs more often than in the input network, divided by the total number of random networks. Therefore, the P -value ranges from 0 to 1. The smaller the P -value, the more significant the motif.

Therefore, the motifs found in the input network are available, including some related statistical measures. As mentioned in the previous step, three different measures are used in this algorithm. There are no exact thresholds for these measures to distinguish motifs; the more restricted the thresholds, the more precise the motif. According to previous experimental results (Milo et al., 2002 ), the following conditions can be used to describe a network motif:

Using 1000 randomised networks, the P -value is < 0.01.

The frequency is larger than four.

Using 1000 randomised networks, the Z-score is > 1.

According to the above conditions, aiming to be as precise as possible, the patterns with significant measures are those that describe the network motifs.

Extracted motifs of the tourist attraction network

The Sina Weibo data analysed in this study encompassed 104 attractions within Suzhou City (Fig. 1a ). According to the processing method described in the previous section, the data from Sina Weibo constituted a total of 2171 edges of the tourism network. When searching for k-motifs, the frequency of (k-1) motifs in the original network should be the same as that in the randomised network (Yu et al., 2020 ). In this study, motifs with more than four nodes did not meet the requirements for extraction; therefore, we extracted motifs with three and four nodes. We determined that a motif appeared in the network based on the criteria mentioned in the previous section. Consequently, we extracted three motifs for three-node motifs and six motifs for four-node motifs, as shown in Figs. 3 and 4 .

Topological types of tourism network motifs.

Frequency and Z-score of tourism network motifs.

Under each motif in Fig. 3 is the proportion of that motif within the network, while the ID of that motif is in the top-right corner of each cell. Each node in the graph also contains a corresponding label, as shown by the IDs of motifs 1 and 4. The node labels of the other motifs are similar. Therefore, we did not label them all to ensure the figure remained concise. Referring to the classification of motifs in previous studies (Costa et al., 2007 ; Yang et al., 2017 ), we divided motifs into four base classes: chain, mutual dyad, double-linked mutual dyad, and fully connected triad. The chain-class motif refers to tourists visiting three attractions sequentially without returning. Similarly, a double-linked mutual dyad motif means that tourists flow in both directions between two pairs of attractions. The fully connected triad motif refers to a pair of three attractions with any two pairs flowing in both directions.

Among them, the mutual dyad, double-linked mutual dyad, and fully connected triad have uplinked and downlinked variants in their main categories. For example, if node A of the mutual dyad sends tourists to another attraction, we use the term ‘uplinked’ to describe this motif, whereas if node A receives tourists from another attraction, we describe the motif as ‘downlinked’. By analogy, these naming rules can be extended to double-linked mutual dyads and fully connected triads. The centrally linked mutual dyad and fully connected triad with a mutual dyad are two more specific variants. The centrally linked mutual dyad is based on a core attraction surrounded by three nodes with mutual circulation; however, none of the three attractions are connected to each other. A fully connected triad with a mutual dyad constitutes one fully connected triad in which one node forms another mutual dyad.

Among the abovementioned nine motifs, those with IDs 1, 2, and 3 are three-node motifs, accounting for 37.61% of the total number of network subgraphs. This indicates that the flow of tourists between any three attractions in the tourism network is dominated by chaining. This suggests that there is an order for most connections among the three attractions’ patterns. The remaining six motifs of the four nodes accounted for 29.67%, with three motifs (those with IDs 4, 5, and 6) forming around the centre point in the lower left corner. The centrally linked motif corresponds to a movement pattern referred to as a ‘basecamp’ in previous studies (Lau & McKercher, 2006 ; Lue et al., 1993 ; Oppermann, 1995 ), in which tourists establish one attraction as their basecamp and leave to visit other places, only to return later. In the downlinked variant, this base camp appeared as a gateway attraction for visiting attractions B and C. The motifs with IDs 7, 8, and 9 were mainly structured as a fully connected triple attraction, which comprised three closely linked attractions where tourists can move freely. In addition to this triple attraction, we also identified a relationship between one attraction and one of three attractions, exhibiting relationships of receiving, conveying, and circulating with each other. Therefore, regarding the connection patterns of the four attractions, the function of the key attractions in it is both specific and vital.

Motif interpretation: Specific attractions

In this study, all calculated motifs were the local mobility patterns of tourists that occurred at high frequencies in the original tourist network. The following analysis was performed for the distribution of attractions on each node of the extracted motifs. We selected the node with the highest degree for each motif and counted the attractions appearing in that node for all subgraphs in the tourist network. The top three attractions with the highest frequency on the highest-degree node were selected according to their frequency, as shown in Table 1 . In the table, the highest-degree nodes are shown in orange.

Table 1 shows that the attractions Guanqian Street, Jinji Lake, and Pingjiang Road are the most highly placed nodes in the subgraph, indicating that these three attractions are in a relatively central position in the whole network. In other words, the network is organised around these three attractions, forming the vast majority of the local tourist movement patterns. Additionally, attractions such as Zhouzhuang and Hanshan Temple also play a key role in the local network. Zhouzhuang appears in the nodes of the motif as an attraction en route to other attractions, acting as a gateway. Generally, in motif patterns 3 and 9, tourists do not return after visiting Zhouzhuang but continue to visit attractions interconnected with Zhouzhuang, which is attributable to the long distance between Zhouzhuang and the urban area of Suzhou. The opposite is true for Hanshan Temple, whose corresponding motif patterns (2, 4, and 7) tend to be the attractions that tourists visited before they went to B through other attractions, implying that tourists all converged at that attraction before visiting other attractions. We further determined the top three attractions in node B: Tongli National Wetland Park, China Flower Botanical Garden, and Dabaidang Ecological Park. The common characteristic of these three attractions is that they all have multiple varieties of flowers with ornamental value and rich vegetation coverage, making them good places for hiking during spring.

Motif interpretation: Types and titles of attractions

In addition to exploring the nodes with the highest degree of motifs, this study examines the types and titles of attractions on each node. According to the classification proposed by another study in Suzhou (Xue & Zhang, 2020 ), attractions can be classified into natural, cultural, and commercial, according to their landscape type; they may also be classified as 5A, 4A and others according to their title (A tourist attraction with a ‘5A’ score implies that it has the most beautiful scenery, the best service and perfect facilities). Fig. 5 shows the distribution of attraction types on the nodes, Fig. 6 shows the distribution of attraction titles on the nodes, while the node labels in the lower right corner of each figure are used to refer to the relative positions of the nodes on each motif. We refer to these node labels in the subsequent paragraphs.

The attraction type of motif nodes.

The attraction title of motif nodes.

Figures 5 and 6 show that the types of attractions on each node differ; however, the differences in the types of node attractions between each motif in each major category are relatively insignificant. This indicates that each major category of attraction connection pattern summarises a common class of tourists’ local movement patterns, while the attributes of each attraction in this movement pattern are fixed.

Types of attractions

In Fig. 5 , the distribution of the attraction types of the nodes differs insignificantly in the type of attraction for the chain-type motif. Regarding the mutual dyad type, the types of node A are still relatively balanced, whereas the types dominated by nodes B and C are different and exactly opposite. Specifically, node B of motif 2 is dominated by natural-type attractions, whereas node C has more than 50% cultural attractions. Conversely, motif 3 shows the opposite pattern. The double-linked mutual dyad exhibited more obvious characteristics. First, we observe that the proportions of nodes C and D attraction types of the three motifs are identical, with the main differences appearing in nodes A and B. Second, for IDs 4 and 5, node A’s commercial attractions exhibit a significantly high proportion, which highlights their role in aggregating tourists in the local network. In contrast, for motif 6, node A primarily serves as a transit node from B to C and D, while its proportion of commercial attractions is not notably high. Lastly, in the fully connected triad, the proportion of interconnected nodes in the three motifs remains consistent. Nodes C and D are dominated by cultural attractions, whereas node A, acting as a communication hub for other attractions, exhibits a more balanced distribution of types.

Titles of attractions

In Fig. 6 , the percentages of 5A and 4A attractions on nodes without attraction titles are significantly lower than those on nodes with famous attraction titles. Specifically, considering the percentage of attraction titles on each node, the difference in the percentage of B and C node levels for the chain-type motif is insignificant. Moreover, they are dominated by unpopular attractions. However, the percentage of A as a transit node for famous attractions is significantly higher than that of B and C. The situation of node A in the mutual Dyad type is similar to that of the chain type, with a higher percentage of famous attractions, whereas the situation is different for nodes B and C. Nodes B and C have the opposite ratio: when node B has a higher proportion of famous attractions, node C is dominated by non-famous attractions, and vice versa. Regarding the double-linked mutual dyad type, the proportion of famous attractions in node A is significantly higher than that in the other nodes. However, there is no significant difference in the percentage of attraction titles in the other nodes, regardless of whether it is a bidirectional flow with node A, and all of them are dominated by non-famous attractions. When there is a fully connected triad in the motif, the proportion of the three nodes that are fully connected as attractions with titles is very high, indicating that the flow of tourists between the 5A and 4A attractions is considerable. In contrast, most of the B nodes that are only connected to A nodes comprise non-famous attractions, indicating that attractions without good titles cannot establish a better connection with 5A and 4A.

We applied network motif analytics as a novel approach for exploring the local structure of tourist networks, based on social media data. The overall structural features of the network emerged from the local relational features. To comprehend the principles of tourist network formation, it is essential to consider not only the overall network perspective but also local network connections. The results showed that attractions play an important role in local networks and that this role is related to the type and level of attractions. Therefore, enhancing the future development of tourism in Suzhou hinges on strategically guiding attractions to fulfill their appropriate service functions within the destination city.

Tourist mobility patterns

This study employs the theory of motifs, which originated from complex network science, as an innovative approach to investigating tourist mobility patterns. Originally used in the field of biology, the network motif algorithm for complex networks was applied in this study to examine the relationships between overabundant tourism mobility patterns and the corresponding attractions. Unlike previous methods for mining travel motifs, the analysis results of motif analysis in the network cannot directly extract tourists’ travel itineraries in the city. The motif-based analysis method focuses more on the movement patterns of groups of tourists between several strongly associated attractions. Based on this characteristic, network motif analysis is more suitable for observing local phenomena.

For tourist movement in urban tourist destinations, a large directed graph can be constructed for tourist movement between attrations according to the concept of network science theory. By applying the motif extraction method to this graph, we can find that it is these simple repeating topologies that make up the overall network (Fig. 7 ). In this study, four classes and nine motifs effectively summarised the diverse mobility patterns. This implies that despite the diversity of their travel history, humans follow simple reproducible patterns (González et al., ( 2008 )). Understanding tourist mobility patterns enhances the comprehension of city destination systems and provides vital insights into city destination planning and development (Ashworth & Page, 2011 ).

Motifs in attraction network.

Connection patterns between attractions

This study examines a tourist network’s connection patterns to identify attractions with distinct roles, including core, transit, and gateway attractions. Tourist attractions within destinations vary in significance, while the hierarchical structure of urban destination systems differs based on their appeal to tourists (Golledge, 1978 ). From a product-marketing standpoint, these findings help marketers understand the function of attractions in tourists’ travel itineraries and establish a foundation for the development of targeted tourism products. For example, for the core attractions, overall tourism planning should be carried out around them; for the identified transit attractions, more transportation routes should be planned for these attractions; for the gateway attractions, the hotel reception services and tourist guide services around these attractions should be enhanced. Nevertheless, the results underscore that the crucial nodes within the nine motifs are almost always composed of a few of the most renowned attractions. From a risk management perspective, an excessively concentrated destination may cause ‘overtourism’ (Peeters et al., 2018 ).

Implications for tourism management

Understanding tourists’ movement is essential for tourism managers to plan and implement effective sustainability strategies (Shi et al., 2017 ). The tourist network examined in this study reflects the movement of tourists among attractions, provides a novel approach to analyse tourist movement patterns within destinations and accurately depicts tourists’ digital footprint within destinations (Fan et al., 2024 ). The results show that although tourists’ detailed movement patterns among destinations are highly complex, local attractions’ connections in the network can be grouped into several patterns. This suggests that although tourists differ in preferences, there are also commonalities in their overall spatial behaviours, which helps enrich the group user profile of visiting tourists. Therefore, destinations can develop attractions and alternative attractions based on the analysis of tourist movement patterns by capturing popular travel routes (Vu et al., 2015 ).

Conclusions

As cities become centres of economic activity, new forms of urban tourism are becoming popular, while an increasing number of tourists are choosing cities as destinations to pursue novel, diverse, and personalised travel experiences (Füller & Michel, 2014 ). This study employs motif analysis in complex network science to elucidate tourist mobility patterns and depict the interconnections between attraction systems in Suzhou, China. We innovatively used actual attractions as network nodes and focused on attraction connection patterns to provide practical implications for destination management. The main conclusions are summarised as follows:

Referring to the motif discovery method known as Kavosh, we extracted nine motifs from a tourist network in Suzhou. These nine motifs can be categorised into four main classes: chain, mutual dyad, double-linked mutual dyad, and fully connected triad.

Specific attractions represented by nodes in the motifs are explored comprehensively. Suzhou’s network is organised around three attractions, namely, Guanqian Street, Jinji Lake and Pingjiang Road, which form most of the local attractions’ connection patterns. Attractions such as the Zhouzhuang and the Hanshan Temple perform specific functions in the network.

The types and proportions of attractions were investigated by visualising local tourist mobility patterns in the network. The results showed that nodes with a higher degree of motifs were generally well-known attractions with titles such as 5A or 4A and were dominated by cultural and commercial attractions.

These results provide a new analytical methodological framework for examining connection patterns in local attraction systems, as well as a basis for the management of attractions within urban tourism destinations.

Despite the theoretical insights and practical applications of this study, there are some limitations that must be acknowledged. For example, social media data are prone to various biases—e.g. the popularity of specific platforms among users—while the amount of data may vary by country, year and population. The bias associated with highly engaged users can result in the overrepresentation of such populations (Encalada-Abarca et al., 2023 ). Additionally, the data used in this study mainly reflect tourists’ spatial behaviour within a city. Because tourists’ spatial behaviour patterns within different destinations differ, it is necessary to use tourism networks within multiple destinations to establish comparisons in subsequent studies, which could help generalise this study’s findings. Future work could further explore the mechanisms of attraction selection by the mobility motifs, such as the fact that tourists are looking to maximise satisfaction when planning their itineraries.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

Special thanks to Prof. Yang Xu for his advice in writing and conceptualization. This study was supported by the National Natural Science Foundation of China under Grant [number 41830645] and Yunnan Provincial Science and Technology Project at Southwest United Graduate School [number 202302AO370012]. The first author - Ding would like to thank to his fiancée, Lanqi Liu, for her help in coding and figure drawing. And her encouragement during Ding’s hard time was key to the final publication of this paper.

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Spatial patterns of tourist flows represent the movement of tourists and show differences in tourism resources giving advice for promoting balanced and sustainable tourism development. This paper proposes a novel framework for analyzing these patterns based on tourists' digital footprint data collected from online travel diaries. Based on illustrative case study data from Qingdao (China), the framework, combining traditional quantitative and social network analysis, is able to pinpoint: (1) The influence of distance decay and attractions’ popularity on the spatial patterns of tourist flows; (2) The uneven distribution of the core tourist nodes and the existence of the structural hole phenomenon, which form a network pattern with unbalanced power and intense internal competition; (3) The formation of the core area for tourism along the coastline – as is typical for coastal tourism cities. This difference of tourism resources between coastal and inland areas, thus, remains a challenge for future tourism development in Qingdao.

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Evolution of international tourist flows from 1995 to 2018: A network analysis perspective

Yuhong shao.

a School of Tourism, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu 610065, China

Songshan (Sam) Huang

b School of Business and Law, Edith Cowan University, Joondalup, WA 6027, Australia

Yingying Wang

Mingzhi luo.

Tourist arrivals and tourism revenues have been extensively studied to evaluate international tourist flows, whereas the structure and evolution of these flows have received less attention. Based on international tourist arrival data from 221 countries/regions during the period 1995–2018, this study applies network analysis to explore the structure and evolution of international tourist flows, and the roles and functions of countries/regions in the international tourist flow network. The results of this study reveal that the network density of international tourist flows is increasing. Countries/regions in Europe, East Asia and North America generally occupy a significantly important position within the international tourist flow network, especially Germany and China. Those geographically close countries/regions demonstrate the same or similar roles and positions in international tourism. This study has significant implications for tourist destination management and marketing.

• • We illustrated the necessity of exploring the structure of international tourist flows.
• • We evaluated the structure and evolution of international tourist flows among 221 countries/regions from 1995–2018.
• • We employed Network Analysis to explore the roles and functions of countries/regions.
• • Germany and China acted as the dominating outbound and inbound tourism markets respectively from the perspective of structure.
• • Geographically close countries/regions demonstrated the same or similar roles and positions in international tourism.

1. Introduction

International tourism has become a popular global leisure activity worldwide ( Keum, 2010 ). According to a report released by the World Tourism Organization (UNWTO), the magnitude of international tourist arrivals rose to 1.4 billion in 2018, ahead of the forecast by UNWTO ( UNWTO, 2019 ). Likewise, the revenues of international tourism increased from US $485.178 billion in 1995 to US $1.649 trillion in 2018 ( UNWTO, 2020 ). In this regard, international tourist flows have attracted the attention of both the global tourism industry and academic research ( Zhang, Li, & Wu, 2017 ). Previous research has mainly evaluated international tourist flows from the perspectives of tourist arrivals or tourism revenues (e.g., Balli, Balli, & Louis, 2016 ; Hall, 2010 ; Huang, Han, Gong, & Liu, 2019 ; Yang, Liu, & Li, 2018 ; Zhang et al., 2017 ).

Essentially, international tourism is a place-oriented activity with tourist flows across country borders ( Deng & Hu, 2018 ; Keum, 2010 ). However, few studies have focused on the structures of international tourist flows worldwide, especially the dynamic changes of these flows. According to Yang et al. (2018) , today's world order faces unprecedented backlash, as does the global tourism industry. Understanding the structure and evolution of the international tourist flows is conducive to the implications for the development of infrastructure, product, destination and others, as well as the management of tourism's impacts on society, environment and culture ( Lew & McKercher, 2006 ), which can be helpful for policymakers and tourism firms to improve market competitiveness and destination management.

Network analysis is an approach with a set of methods and tools to map and measure the patterns, flow and strength of relationships between actors ( Casanueva, Gallego, & García-Sánchez, 2014 ), and has been applied in the study of tourist flows within a region or between selected countries ( Zeng, 2018 ). Following previous studies, from a global perspective, this study employs this approach to investigate the roles and functions of countries/regions acting as tourism origins or destinations during the period 1995–2018, further revealing the evolution of the international tourist flow structures. The rest of this paper is structured as follows: The next section provides a brief review on tourist flow. The following section reports the data sources and methodology. Then, the results, discussion and conclusions are presented. The last section provides implications as well as future research and limitations of this paper.

2. Literature review

2.1. tourist flow.

According to Leiper (1979) , tourism system involves five elements, namely tourists, a tourist industry, original regions, transit routes and destination regions. In this regard, tourist flow refers to the movement of tourists from an origin place, through transit regions, to a destination and the stay of tourists in these regions ( Oppermann, 1995 ; Zeng, 2018 ). According to Bowden (2003) , tourist movement encompasses three basic elements: intensity, direction and pattern. Generally, the intensity is analyzed under the fields of “tourist demand” or “tourism forecasting” since it is related to the volume and frequency of tourist flows. Direction and pattern, which reflect the static and the dynamic elements of tourist flows among regions, respectively, are usually discussed under the term “tourist flow” ( Bowden, 2003 ). The dynamic element mainly centres on the flows between origin and destination regions. In contrast, the static element is composed of several factors, such as tourism destinations, overnight stays, accommodation types, and the gateways between origin and destination regions ( Oppermann, 1992 ).

A large amount of research has been conducted on tourist flow at different geographic scales ( Amelung, Nicholls, & Viner, 2007 ). The geographic scale reflects the hierarchy and functional arrangements of spatial issues, which is important for exploring tourist flow ( Bowden, 2003 ). According to Xia, Zeephongsekul, and Arrowsmith (2009) , the geographic scale of tourist flow can be attributed to the macro- and micro-levels based on distance. The macro-level refers to a relatively large distance of hundreds of kilometres ( Xia et al., 2009 ), which is often regarded as inter-destination movement pattern ( Lau & McKercher, 2006 ). In contrast, the micro-level is considered to be a relatively short distance, such as from an attraction to another attraction, which refers to intra-destination movement pattern ( Lau & McKercher, 2006 ). As far as the geographical scale of tourist flow is concerned, this study estimates the tourism flows between 221 countries/regions around the world from the macro-scale or inter-destination movement perspective.

2.2. Measurement of tourist flow patterns

The pattern of tourist flow involves various items of information, which is conducive to designing tourist packages, providing attractive combinations of attractions, proposing tourism guidance policies and marketing management ( Lew & McKercher, 2006 ; Xia et al., 2009 ). A large amount of research has attempted to map tourist flow through various methods ( Leung et al., 2011 ). The traditional techniques for tracking tourist flows mainly depend on observations, interviews or questionnaires ( Zeng, 2018 ). Researchers are asked to track the tourists' movements to develop a map of tourists' distribution within a given destination ( Dumont, Roovers, & Gulinck, 2005 ). In addition, tourists are required to retrace their movements through self-administered questionnaires ( Xia et al., 2009 ). However, limited by time and cost, these techniques usually obtain a limited amount of data and lack the needed accuracy. With the development of technology, new tracking techniques are applied to record the information of tourist flows, such as the Global Positioning System (GPS) and land-based tracking systems, which have proven to be effective tools for estimating the spatial flows of tourists over time ( Shoval & Isaacson, 2007 ).

However, the above two kinds of techniques, namely the traditional techniques and new tracking techniques, are applied to tourist flows at the micro- or meso-level. Regarding the macro-level, the panel data published by organizations are regarded as important sources for researching tourist flows ( Liu, Li, & Parkpian, 2018 ; Lozano & Gutiérrez, 2018 ; Su & Lin, 2014 ). The large amounts of data available, the use of the same statistics definitions, easy accessibility and long-term data availability, are considered as the main advantages of panel data, which contributes to the wide use of panel data in exploring international tourist flows. For example, Li, Meng, and Uysal (2008) explored the tourist flows among the Asia-Pacific countries for the years of 1995 and 2004. Based on panel data from 1990 to 2002, Keum (2010) examined the patterns of international tourist flows between South Korea and its 28 major trading partner countries.

Additionally, scholars have applied several methods, related to data mining methods and statistical methods, to identify the spatio-temporal patterns of tourist flows, including but not limited to the field of international tourist flows. These methods involve the Clustering Method ( Asakura & Iryo, 2007 ), Gross Travel Propensity Index (GTP) ( Li et al., 2008 ), Geographic Information System (GIS) Analysis ( Connell & Page, 2008 ), and Markov Chains ( Xia et al., 2009 ). For example, Asakura and Iryo (2007) applied the Clustering Method to reveal the topological characteristics of the tourist movement in Kobe, which contributes to finding the hidden behaviour of tourists. Connell and Page (2008) employed GIS analysis to map car-based tourist flows in Loch Lomond and Trossachs National Park. Xia et al. (2009) employed Markov chains to estimate the outcomes and trends of events related to the patterns of tourist flows across Phillip Island, Australia.

Recently, scholars have introduced and applied network analysis to reveal a relatively comprehensive picture of international tourist flows (e.g., Leung et al., 2011 ; Zeng, 2018 ). Compared with other statistical methods (e.g., Clustering Method), the tourist flow network based on network analysis can be visualized and is easy to understand ( Leung et al., 2011 ). Accordingly, network analysis reveals the roles, functions and cohesiveness groups of destinations, which provides more implications for destination managers ( Kang, Lee, Kim, & Park, 2018 ; Scott, Cooper, & Baggio, 2008 ).

2.3. Network analysis and international tourist flows

Mainly based on mathematics and graph theory, network analysis is an approach that uses a set of methods and tools to map and measure the patterns, flow and strength of relationships between actors ( Casanueva et al., 2014 ), which makes network analysis different from other analysis methods ( Scott et al., 2008 ). The relationships can be of various types, including but not limited to goods, services, information, and social support; the actors establishing relationships with each other can be individuals, organizations and other linked information/knowledge entities ( Haythornthwaite, 1996 ). Although the network analysis technique was mainly developed in economic sociology, researchers have applied mathematical models to estimate the structures of various relationships, indicating that network analysis was not limited to the social field ( Scott, 1991 ). Moreover, researchers like Granovetter (1973) , Burt (1992) , Watts (1999) and Lin (2001) , have furthered the research on network analysis, making it widely used in various fields.

Currently, scholars have employed network analysis to estimate the flow paths and patterns of international tourists within a destination. In these studies, a destination is regarded as an actor within the tourist flow network, while tourists from one destination to another is viewed as the relationship between destinations ( Zeng, 2018 ). For example, Leung et al. (2011) utilized network analysis to analyze the pattern of overseas tourist flows in the most visited tourist attractions throughout the Olympics in Beijing. Likewise, Zeng (2018) estimated the structure and characteristics of Chinese tourist flows in Japan through itineraries from travel services and trip diaries. Lozano and Gutiérrez (2018) explored the structure and interactions between source and destination markets in the global tourism network in 2013. Wu, Wang, and Pan (2019) combined numerical simulation and network analysis to construct an agent-based network of inbound tourism in China and numerically investigated the responses of the inbound tourist flows in some scenarios of practical significance.

However, these above-mentioned studies, mainly centring on particular regions or selected countries or specific year, hardly contribute to the understanding of the competitiveness of destination countries/regions worldwide. In this regard, the purpose of this study is to estimate the structures and evolution of international tourist flows during the period 1995 to 2018 from a global perspective, which will be further conducive to proposing general tourism development planning for most countries/regions worldwide.

3. Data and methodology

3.1. data source.

The annual data for bilateral tourist flows were collected from the UNWTO, which covers 221 countries/regions from 1995 to 2018. This data set is compiled by destination countries/regions based on the number of inbound tourists. The data for 1995 and 2018 are the earliest and latest data sets that can be obtained, respectively. Although this data set is widely used in the field of international tourism ( Balli et al., 2016 ; Yang et al., 2018 ; Zhang et al., 2017 ), three issues in the data set need to be emphasized. First, different destination countries/regions adopt different definitions in statistics. Among 8 statistics definitions currently adopted by destination countries/regions, the most commonly used statistics are arrivals of non-resident tourists at national borders (by country of residence), arrivals of non-resident tourists at national borders (by nationality), arrivals of non-resident visitors at national borders (by country of residence), and arrivals of non-resident visitors at national borders (by nationality). Following the study of Yang et al. (2018) , when cleaning the data, this study gave preference to the above definitions associated with border; 4 other definitions related to accommodation were considered when the above four border-based statistics were missing. Second, different countries/regions had different tourism statistics systems, and several countries/regions reported data for a subset of origin countries/regions. Third, this study unified the names of countries/regions to avoid the ambiguity caused by different statistical systems, such as unifying “State of Palestine” into “Palestine” and “Congo, Democratic Republic of the” into “Democratic Republic of the Congo”.

3.2. The theoretical-methodological framework of network analysis

Network analysis aims to analyze the structure of relationships (displayed by links) between given entities (displayed by actors) in social or economic phenomena ( Haythornthwaite, 1996 ). It employs a set of techniques to explore the characteristics of a whole network, as well as the roles and positions of these entities within the network ( Shih, 2006 ). In this study, we applied network analysis to explore the structure of international tourist flows, where the countries/regions are treated as “actors”, the tourist routes between origin and destination countries/regions are regarded as “links”. Fig. 1 shows a simple case with five countries/regions (labelled A, B, C, D and E) . Fig. 1 A is a network graph, showing the relationship of international tourism among these five countries/regions. For example, tourists from country/region A visit C, D and E, and do not travel to B; additionally, country/region A only receives tourists from D. According to the graph, an asymmetric matrix can be built (see Fig. 1 B), in which a row represents the destination countries/regions and a column stands for the origin countries/regions.

A simple case with five actors.

This type of matrix above merely describes the presence or absence of the given type of relationship. However, each route between two countries/regions carries a specific number of tourists, which is considered as “weightings”, yielding a valued matrix. To be specific, the ( i , j )th cell (row i , column j ) carries a number that represents the number of outbound tourists from country/region i to country/region j . On this basis, the matrix of 221 countries/regions used in this study is constructed. The rest of this section introduces the indicators of network analysis which are appropriate for this study.

To estimate the structure of international tourist flows, this study applied three indicators of network analysis, namely density, degree centrality and blockmodel. Among them, density is the main indicator for the structure of a whole network ( Casanueva et al., 2014 ), while degree centrality and blockmodel are important indicators to examine the structure of actors within a network ( Borgatti, Everett, & Johnson, 2018 ).

To be specific, density is a measure of cohesion, which means the connectedness of a network. This indicator can be interpreted as the probability of a link between each pair of randomly selected actors ( Borgatti et al., 2018 ). Degree centrality, which is measured by the number and value of links that an actor has, is suitable for analyzing the structural roles and positions of each actor within this network. However, although degree centrality is the primary indicator for the structure of an actor, it cannot contribute to understanding the importance of links between actors ( Asero, Gozzo, & Tomaselli, 2015 ). Thus, we enriched the analysis by employing the blockmodel.

The term “blockmodel” was first proposed by White, Boorman, and Breiger (1976) to explain the social structure in terms of interconnections among actors within a social network. Two actors that occupy the same structural roles or positions in a network are said to be structurally equivalent ( Asero et al., 2015 ), and are grouped into the same block. Thereby, a network is divided into different “blocks”. A “block” is a subnetwork embodied in the overall network, and the actors within a “block” are structurally indistinguishable because they have the same external relationships. This implies that the actor with the same role or position in different links can be interchangeable with one another. According to Borgatti et al. (2018) , the analysis for structural equivalence provides a high-level description of the links within a network. Moreover, structurally equivalent actors share other similarities as well; they show a certain amount of homogeneity ( Borgatti et al., 2018 ). Considering the competition in tourism market and alternative tourist flow routes, it is necessary to analyze structural equivalence when studying tourist flows ( Asero et al., 2015 ).

The formulas of these indicators have been explained by Knoke and Kuklinski (1982) , Scott (1991) , Carrington, Scott, and Wasserman (2005) , Knoke and Yang (2008) , Luo (2012) , Borgatti et al. (2018) , among others. In this regard, we explained the above indicators in the context of international tourist flow ( Table 1 ). The indicators of network analysis used in this study were calculated by UCINET 6.6.

Explanation of indicators used in this study.

Source: Knoke and Kuklinski (1982) , Scott (1991) , Carrington et al. (2005) , Knoke and Yang (2008) , Luo (2012) , Borgatti et al. (2018) .

4.1. Structure of the whole international tourist flow network

Network density indicates the extent to which countries/regions interact with other countries/regions in terms of international tourism. Fig. 2 shows the network density of international tourist flows among 221 countries/regions for each year from 1995 to 2018. On the whole, the international tourist flow network was a sparse network with low density. The network density significantly increased, with the value of 2018 (0.0299) being more than twice that of 1995 (0.0108), which demonstrates the increasing travel connections among countries/regions. As seen from Fig. 2 , the increasing trend in density can be divided into five phases: periods of rapid growth from 1995 to 2000, from 2003 to 2007, and from 2009 to 2018, and periods of fluctuation from 2000 to 2003 and from 2007 to 2009. The period of fluctuation in density coincides with the period of major crisis events, such as the 9/11 terrorist attacks in 2001, the severe acute respiratory syndrome (SARS) in 2003 and the financial crisis in 2008. Additionally, the trend of density was essentially consistent with that of international tourist arrivals as listed in Fig. 2 . After 2009, the number of international tourists and the network density of international tourist flows continued to increase significantly.

The number of international tourists and the density of the international tourist flow network from 1995 to 2018.

Moreover, Gephi software was applied to visualize the distribution of international tourist flows. Fig. 3 and Fig. 4 show the international tourist flow networks in 1995 and 2018, respectively. The larger node size indicates that a particular country/region generate more international tourists, while the thicker line between countries/regions represents more outbound tourists. As shown in Fig. 3 , in 1995, the largest cluster for the international tourist flow network was Europe, followed by North America and East Asia, with several countries as the centre, including the United States, Germany, Canada, France, and the United Kingdom. A large number of countries/regions were at the edge of the international tourist network and had only a few travel links with the remaining countries/regions within this network in 1995. While in 2018, we can find that after 24 years, almost all countries/regions have strengthened travel links with other countries/regions, which suggests that the interconnectedness of the international tourist flow network has been largely improved. Although Europe, North America and East Asia were still the most important clusters, the number of countries/regions covered in these clusters had increased significantly.

The global tourist flow network in 1995.

The global tourist flow network in 2018.

4.2. Structure of countries/regions within the international tourist flow network

4.2.1. the roles and functions of countries/regions in outbound tourism.

Out-degree centrality was used to describe the role and function of a country/region in outbound tourism. The results of the out-degree centrality are summarized in Fig. 5 . During the study period, out-degree centrality values of most countries/regions were on the rise, with occasional fluctuations in 2001, 2003 or 2008, and maintained a relatively stable ranking. Thus, Table 2 only reports the values of out-degree centrality and rankings of countries/regions in 2018 because of space limitations.

Result of the out-degree centrality for 221 countries/regions from 1995 to 2018.

The centrality analysis of countries/regions in the international tourist flows in 2018.

Given that countries/regions in the top 30 account for about 80% of the sum of out-degree centrality in the 221 countries/regions, we considered these countries/regions occupied a relatively important role and function in outbound tourism. Over the 24 years, Germany, the United Kingdom, France, Switzerland, Czech Republic, Italy, Belgium, Austria, Spain, the Netherlands, Ukraine, the Russian Federation, the United States, Canada, Mexico, China, Hong Kong SAR, Macao SAR, Taiwan, Japan and Australia were always in the top 30, playing a dominant role in generating international tourists to many destination countries/regions. In particular, among these 21 countries/regions, 12 of them, including Germany and the United Kingdom, belong to Europe; the United States, Canada and Mexico are countries in North America; China, Japan, Hong Kong SAR, Macao SAR, Taiwan are located in East Asia; and Australia belongs to Oceania.

Specifically, during the period 1995–2018, Germany always ranked 1st, with an average out-degree centrality value of 107.444, indicating that Germany plays a leading role in global outbound tourism, taking into account the number of destination countries/regions and outbound tourists. In particular, before 2000, the out-degree centrality values of Germany were far higher than those of the United States, which ranked 2nd. The out-degree centrality values of the United States showed a relatively stable upward trend from 1995 to 2013 and a sharp increase after 2013. During 2002 and 2015, the out-degree centrality values of Hong Kong SAR surpassed those of the United States, ranking 2nd in the international tourist flow network, and maintained a slight upward trend after 2007. France, Canada and Italy maintained a relatively stable ability to interact with other destination countries/regions and showed a steady growth trend throughout this study period.

Regarding the Russian Federation, its out-degree centrality value showed an increasing trend until 2013, after which it began to decrease sharply. This may be related to the sharp decline of oil price, the Ukraine crisis in 2014 and the sanctions imposed by Western countries, which make the economy stagnant in the Russian Federation and further have a negative impact on tourism ( Dreger, Kholodilin, Ulbricht, & Fidrmuc, 2016 ). It is worth noting that the out-degree centrality value of China continued to significantly increase from 1995 (4.836 of out-degree centrality) to 2018 (113.394 of out-degree centrality), with only a slight decrease in 2008 due to the financial crisis; moreover, the ranking of China showed an upward trend from 21st in 1995 to 3rd in 2018. Besides, after the financial crisis in 2008, the growth of out-degree centrality of most countries/regions slowed, while China showed a trend of unprecedented growth to generate outbound tourists to increasing destination countries/regions.

4.2.2. The roles and functions of countries/regions in inbound tourism

In-degree centrality was used to describe the role and function of a country/region in the inbound tourism network. As shown in Fig. 6 , in-degree centrality values significantly varied in 221 countries/regions between 1995 and 2018. As a whole, the in-degree centrality values of most countries/regions fluctuated upward and maintained relatively stable rankings. Similar to out-degree centrality, the fluctuations in the vast majority of countries/regions occurred in 2001, 2003 or 2008. From 1995 to 2018, Poland, Italy, France, the United Kingdom, Spain, Austria, Germany, the Russian Federation, Turkey, Greece, Netherlands, the United States, Mexico, Canada, China, Hong Kong SAR, Macao SAR, Singapore and Thailand remained in the top 30, with a strong aggregation function for international tourist flows around the globe. Generally, countries/regions in Europe, East Asia, Southeast Asia and North America ranked in the top. Besides, Latin America ranked in the middle, and Africa and Oceania, with several exceptions (e.g., South Africa, Egypt, Australia, New Zealand), had low in-degree centrality values out of the 221 countries/regions over 24 years.

Result of the in-degree centrality for 221 countries/regions from 1995 to 2018.

Specifically, the in-degree centrality values of China had extremely significant growth, and China surpassed Poland to become the leading tourist destination in the international tourist flow network in 2000. Moreover, China, which reached 158.449 of in-degree centrality in 2018 ( Table 2 ), maintained or increased most connections with other origin countries/regions and had the strongest ability to attract international tourists compared with the rest of the world. Concerning other leading destination countries/regions, the in-degree centrality values and rankings of Spain, the United States, Italy, France and Poland have remained close to each other since 2001, and ahead of other countries/regions, including the United Kingdom, which has almost always ranked around 7th out of 221 countries/regions since 1997. As the most important inbound tourism destination in the last century, Poland's in-degree centrality values fell sharply between 1999 (89.070 of in-degree centrality) and 2002 (50.691 of in-degree centrality), and between 2007 (66.085 of in-degree centrality) and 2009 (53.597 of in-degree centrality). The United States showed a similar trend to Poland in terms of in-degree centrality, but more smoothly during the study period. It is worth noting that Southeast Asian countries, such as Thailand, Malaysia, Singapore, Vietnam and Indonesia, were at the forefront of the inbound tourist flow network, consistent with the national positioning created by these countries. Moreover, countries/regions with regional conflicts or infectious diseases had low in-degree centrality values, such as Sudan, Chad, Palestine and the Central African Republic. Tourists throughout the world rarely visit these countries/regions considering their safety.

Furthermore, the rankings of out-degree centrality of countries/regions were relatively consistent with those of their in-degree centrality within the international tourist flow network. For example, countries/regions with high out-degree centrality tended to have high in-degree centrality in the international tourist flow network, including but not limited to Germany, the United States, China, the United Kingdom, France, Japan, Canada, Hong Kong SAR, Italy, Spain, Macao SAR and the Russian Federation, which are concentrated in Europe, East Asia and North America. Moreover, the out-degree centrality in countries/regions with small in-degree centrality also tended to be small, such as Sierra Leone, Montserrat, and Seychelles ( Table 2 ). Most of the countries/regions mentioned above are located in Africa or are islands with small populations and territories. However, the majority of countries in Southeast Asia, including Thailand, Indonesia and Malaysia, had higher rankings in in-degree centrality than that of out-degree centrality during the years of the study, revealing that inbound tourism was a significant pillar of the growth strategies of these countries. Besides, except for countries/regions (e.g., French Guiana, Pakistan, Iraq) that did not have statistics on inbound tourist arrivals, the values of out-degree centrality in several countries/regions (e.g., Republic of Moldova, Belarus, Belgium) were higher than those of in-degree centrality, indicating that these countries/regions have a stronger ability to generate international tourists to many countries/regions than to attract tourists.

4.2.3. The substitutability of countries/regions

The above subsections mainly reveal the role and function of a country/region in the international tourist flow network and do not allow for the importance of travel links between countries/regions to be understood. Therefore, following the study of Asero et al. (2015) , this study used the CONCOR algorithm to estimate the structure corresponding to the country/region's role and position in the network. Countries/regions with the same tourist flow routes can be clustered into one block, indicating that countries/regions in the same block are structurally equivalent and can be substituted for each other ( Luo, 2012 ). Also, CONCOR algorithm provides the density of each of the blocks ( Borgatti et al., 2018 ), and allows for identifying the main links from the values of the density matrix ( Asero et al., 2015 ). Since the roles and positions of countries/regions in the international tourist flow network are relatively stable, countries/regions in each block have not changed significantly over the years. Therefore, we took the results of the 2018 CONCOR algorithm as an example ( Table 3 and Table 4 ).

Members of each block for 2018.

Density within and between blocks for 2018.

Block 1 centred on the Russian Federation and Belarus, and mainly included countries/regions distributed around the Caspian Sea (e.g., Turkmenistan, Kazakhstan, Islamic Republic of Iran). The majority of countries/regions within this block had a higher value of out-degree centrality than that of in-degree centrality, especially Belarus and Republic of Moldova. Moreover, as shown in Table 4 , countries/regions in Block 1 were closely linked to each other (Density = 0.179) and interacted with countries/regions in other blocks except for Block 3. This suggests that most countries/regions within Block 1 have a strong ability to generate international tourists to both countries/regions in other blocks and its own block. The majority of countries/regions in Block 2 are concentrated in North and Central Africa (e.g., Algeria, Sudan, Djibouti) and Arabian Peninsula (e.g., Saudi Arabia, Kuwait, Yemen); that is, around the Red Sea. Except for Saudi Arabia, the degree centrality of countries/regions within this block generally ranked in the middle or lower among 221 countries/regions, indicating that these countries/regions have a medium performance in international tourism.

As for Block 3, except for French Guiana and Guadeloupe, which are French overseas regions, other 16 countries, including Burundi and Mozambique, mainly belong to Central or Southern Africa. Most countries/regions in this block had low values of degree centrality and barely had travel links with other countries/regions, suggesting that these countries/regions are at the edge of international tourism flow network. According to Block 4, except for Suriname, 23 countries/regions in this block are around the Central or Western Pacific (e.g., China, Malaysia, Indonesia), and the remaining countries/regions are located around the Gulf of Guinea (e.g., Côte d'Ivoire, Liberia). Asian countries/regions in Block 4 generally ranked higher than those in Africa and Oceania in terms of degree centrality. Besides, the interactions between countries/regions within this block (Density = 0.252) were much higher than those with countries/regions in other blocks.

Countries in Block 5 are located in Europe, such as Greece, the United Kingdom, Ukraine, Belgium, Germany and France. Generally, European countries have small territories, developed economies and high affluence rankings in the world. These countries not only generate international tourists but also have the ability to attract tourists from other countries/regions. Besides, its block density was the highest, reaching 0.328, revealing the close connections between European countries. In Block 6, countries/regions are mainly distributed along the Mediterranean Sea (e.g., Tunisia, Morocco, Egypt, Malta, Spain) and the West or North Indian Ocean (e.g., Seychelles, Madagascar, Maldives, Sri Lanka), while a few countries/regions are concentrated in the Gulf of Guinea (e.g., Togo, Congo). The majority of countries/regions in Block 6 had higher rankings in in-degree centrality than that of out-degree centrality, indicating that these countries/regions have a stronger ability to attract international tourists.

Block 7 was dominated by the United States, Canada, and Mexico. Except for the above three countries, other 35 American countries/regions within this block possessed medium or small degree centrality, such as Cayman Islands, Aruba, Colombia. Other countries/regions in Block 7 are located in Asia (e.g., Qatar, Armenia, Israel, Philippines, Nepal), Africa (e.g., United Republic of Tanzania, Ethiopia), Oceania (e.g., Kiribati, French Polynesia) and Europe (i.e., Iceland). Countries/regions within this block has established travel links with countries/regions in the other seven blocks, especially with European countries in Block 5. Block 8 focused on countries/regions located in South America (e.g., Argentina, Brazil, Venezuela, Bolivia), and islands located in Oceania or Western Pacific, including but not limited to Australia, New Zealand, Japan, Cook Islands, Fiji, and Palau. As shown in Table 4 , countries/regions within Block 8 mainly interacted with other countries/regions in its block as well as Block 7 and Block 5.

From the above analysis, countries/regions within the same block are mostly located on the same continent or are geographically close to each other. Geographic contiguity, language similarity or colonial links between two countries/regions increase the bilateral flow of tourists ( Yang et al., 2018 ). It is worth noting that countries/regions located in a block have the same external tourist flows, and the substitution effect refers to the structurally equivalent relationship between countries/regions. In the real situation, every country/region has uniqueness attributes in nature, culture and other aspects that cannot be replicated by other countries/regions within the same block.

5. Discussion and conclusions

Recent years have seen the rapid development of international tourism. The number of international tourists and the amount of tourism revenues are measures of international tourism from the quantity point of view (e.g., Su & Lin, 2014 ; Balli et al., 2016 ; Liu, Li, and Parkpian, 2018 ); however, the network structure and evolution of international tourist flows lack attention. Essentially, international tourism involves cross-border activities ( Deng & Hu, 2018 ). Given the move toward globalization, the order of international tourism is constantly changing ( Yang et al., 2018 ) and can be revealed by the movement of international tourists. Identifying the structure and evolution of international tourist flows is critical for understanding the changes in the past and for formulating effective strategies for future tourism development ( Lew & McKercher, 2006 ). In this regard, based on network analysis, this study empirically evaluates the evolution of international tourist flows between 221 countries/regions during the period 1995–2018 from the perspective of structure, rather than tourist arrivals or tourism revenues.

Network analysis is an approach used to map and measure the flow paths of resources between actors within a network system ( Zha, Shao, & Li, 2019 ), which is suitable for exploring the movement of international tourists. Currently, scholars have applied this approach to the study of tourist flows ( Zeng, 2018 ). However, studies have been limited to a specific region, such as China ( Leung et al., 2011 ) and Sicily ( Asero et al., 2015 ), and lack a global perspective with few exceptions (e.g., Lozano & Gutiérrez, 2018 ). Moreover, these studies mainly centre on a specific year (e.g., Lozano & Gutiérrez, 2018 ; Zeng, 2018 ). Great changes have taken place and are ongoing in the world order since the last century, which has also had a profound impact on international tourism ( Yang et al., 2018 ). Thus, this study applies network analysis to explore the roles, functions and evolutions of countries/regions over the world in tourism flow networks, thereby enriching the study of tourist flows from a global perspective. Understanding the structure and evolution of international tourist flows can be useful for improving market competitiveness and destination management.

This study constructs the international tourist flow network and attempts to reveal the structure and evolution of this network from two levels: the whole network and actor. As for the whole network, the estimated results of the density indicator show that the international tourist flow network is a sparse network, but its density is on the rise. This is related to globalization ( Keum, 2010 ) and government policies ( Deng & Hu, 2018 ), among other factors. This finding echoes the conclusion in the study of Friedman (2005) that the world is flat. Moreover, according to Var, Schlüter, Ankomah and Lee (1989) and Becken and Carmignani (2016) , globalization promotes international tourism around the world, whereas international tourism contributes to globalization, making tourism a real force for world peace.

Moreover, there are fluctuations in the growth in the network density, especially for years 2000 to 2003 and 2007 to 2009, which can be attributed to crisis events, including the September 11 attacks in 2001 and their aftermath ( Dragouni, Filis, Gavriilidis, & Santamaria, 2016 ), SARS in 2003 ( Ritchie, 2008 ), the financial crisis in 2008 ( Hall, 2010 ), the influenza A (H1N1) epidemic in 2009 ( Lee, Song, Bendle, Kim, & Han, 2012 ), and other factors. It should be noted that the studied period has seen several crisis events, including but not limited to the above-mentioned ones. However, only global crises, especially global public health crises, have an impact on the structure of the international tourist flow network. In this regard, we can forecast that the coronavirus disease 2019 (COVID-19), which continues to spread rapidly across the world, has led to a decline in the network density of international tourism flows.

In terms of the actor, the role and function of a country/region in the international tourist flow network are identified utilizing the degree centrality indicator. The roles and functions of countries/regions within the outbound tourist network are a reflection of a country's economic development ( Li et al., 2008 ), the level of openness ( Liu, Li, & Li, 2018 ), price competitiveness index ( Seetaram, Forsyth, & Dwyer, 2016 ), government policy ( Li, Harrill, Uysal, Burnett, & Zhan, 2010 ) and population ( Li, Shu, Tan, Huang, & Zha, 2019 ), while those of the inbound tourist flow network are related to tourism competitiveness ( Mou et al., 2020 ), tourist attractions ( Su & Lin, 2014 ) and culture ( Yang & Wong, 2012 ), among others.

Specifically, among these 221 countries/regions, Germany, Italy, the United Kingdom, France, Spain, Austria, the Russian Federation, the United States, Canada, Mexico, China, Hong Kong SAR and Macao SAR are among the top tourist-generating and receiving countries/regions from 1995 to 2018 and are regarded as the core actors within the international tourist flow network. This finding is consistent with the study of Lozano and Gutiérrez (2018) . These 13 countries/regions are concentrated in Europe, East Asia and North America, with vast territories (e.g., the Russian Federation, Canada, the United States), developed economies (e.g., Germany, the United States, France), relative political stability (e.g., Germany, China, the United Kingdom) or large populations (e.g., China, Mexico, the United States) on the whole ( Li et al., 2008 ).

Germany, in particular, plays a leading role in the global outbound tourism market from 1995 to 2018, while China has acted as the dominating inbound tourism market since 2000 when considering the number of destination/origin countries/regions and international tourists. The Henley & Partners Visa Restriction Index shows that German passports are among one of the most valuable passports worldwide. For example, German passport holders can visit 176 countries worldwide visa-free in 2017 ( Henley & Partners, 2017 ). According to Wu et al. (2019) , China gave priority to inbound tourism from 1949 to 2008 for both political and economic reasons. Recently, China has developed government policies concerning tourism, such as the Belt and Road Initiative, largely enhancing the inbound tourism market and even changing China's inbound tourism market landscape ( Huang et al., 2019 ). Moreover, other factors, including China's thriving history and culture ( Lim & Pan, 2005 ), cannot be ignored.

Countries/regions that do not perform well within the international tourist flow network over the years are mainly located in Africa or on islands, such as Montserrat and Niue, with performances affected by safety concerns, transportation accessibility or small populations. This finding echoes the study of Li et al. (2008) . Besides, the majority of countries in Southeast Asia (e.g., Thailand and Malaysia) have relatively well-developed inbound tourism compared with outbound tourism due to the availability of abundant tourism resources, government support (e.g., the proposal of the Malaysia Tourism Transformation Plan) ( Liu et al., 2018 ), a vast diversity of tourism products ( Liu et al., 2018 ) and a relatively low exchange rate ( Seetaram et al., 2016 ). Moreover, a small majority of countries/regions, such as the Republic of Moldova, Belarus and Sweden, play relatively more important roles and functions in outbound tourism than inbound tourism over the years.

Regarding the structure corresponding to the country/region's role or position in the network, the CONCOR algorithm estimates the structurally equivalent countries/regions of the international tourist flow network flows in 2018. Most countries/regions with similar or the same external links in terms of international tourism are located on the same continent or are geographically close. This finding is in line with the study of Lozano and Gutiérrez (2018) that the clustered structure is determined by geographical factors. Geographically close countries/regions have similar natural, cultural and political environments, which can affect the tourism industry ( Yang et al., 2018 ). For example, Narayan, Narayan, Prasad, and Prasad (2010) noted that Pacific Island countries, especially Fiji, the Solomon Islands and Papua New Guinea (in Block 8 of this study), have similar natural disasters and political instability, which can influence the choices of international tourists. Given the fierce competition in the international tourism market, countries/regions that are structurally equivalent need to provide different kinds of leisure products to be differentiated to international tourists.

6. Implications, limitations and further research

The policy implications are clear and of great significance. First, policymakers should analyze international tourist flows not only from the perspective of tourist arrivals and tourism revenues but also from the perspective of network structure. Future policies should be proposed, such as establishing partnerships with more countries/regions, to address the problems related to tourism in the increasingly globalized world. Second, policymakers should manage tourism routes, plan tourism facilities and define marketing strategies by identifying the roles and functions of countries/regions within the international tourist flow network. Third, according to the results of this study, countries/regions that are geographically close have similar or the same international tourist flow structures. Thus, differentiated tourism products should be provided to create a unique and competitive tourism image for a country/region.

It is important to note that this study has several limitations. First, the data used in this study is compiled by destination countries/regions, each of which may adopt distinct definitions of tourism and may collect tourist arrival data differently. Currently, there are 8 statistics definitions related to national borders or accommodation establishments, which may affect the accuracy of the data set used in this study. Second, due to the use of different tourism statistics systems, several countries/regions only reported data for a subset of origin countries/regions, leading to missing values in the data set. Third, limited by the research goal, it is difficult to analyze every country/region in the world, which may ignore some important evaluations of individual countries/regions.

Implications for future research involve a more in-depth exploration of the international tourist flow network. According to Welch, Welch, Young, and Wilkinson (1998) , as actors define various elements of the network and interact with the external environment, relationships between actors are constantly shifting. In other words, networks are dynamic, and links between countries/regions are both built and lost. Future research is needed to examine the factors (e.g., visa, air transportation) that affect the structure, and how the structure influences the socio-economic development of a country/region. Second, it is an exciting research endeavour to apply network analysis to establish relationships among different agents related to international tourism (e.g., air carriers, tourism service providers in destination). Third, considering the impact of crises on the tourism system, future research should focus on the impact of global crisis events (e.g., COVID-19) on the structure of international tourism (e.g., redistributing power and other resources in the network), and recovery measures.

Declaration of Competing Interest

Acknowledgements.

This study was supported by a grant from the National Social Science Fund of China (17XGL012).

Biographies

Yuhong Shao is a doctoral candidate in the School of Tourism, Sichuan University, P.R. China. Her research interests include outbound tourism, tourism employment and tourism economics.

Songshan (Sam) Huang is Professor of Tourism and Services Marketing in the School of Business and Law, Edith Cowan University, Australia. His research interests include Chinese tourist behaviours, destination marketing, tour guiding and various aspects of China tourism issues.

Yingying Wang is a master student in the School of Tourism, Sichuan University, P.R. China. Her research interests include outbound tourism, tourism education and hospitality management.

Zhiyong Li is a professor as well as the Dean of School of Tourism, Sichuan University, P.R. China. His research interests center on tourism marketing, outbound tourism and hospitality management.

Mingzhi Luo is a lecturer in the School of Tourism, Sichuan University, P.R. China. His research interest includes tourism economics and tourism policy. He is currently working on tourism recovery affected by natural disasters.

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American tourist dies after sudden illness during excursion on Sicily’s Mount Etna, rescuers say

ROME (AP) — Italy’s alpine rescue service says a 55-year-old American tourist has died after being taken ill during an excursion on on the southern side of Mount Etna. Rescuers said the cause of his illness was unknown, but warned against the risk from high temperatures coupled with humidity that may prove dangerous for tourists who usually have no specific preparation for such excursions. After being alerted on Thursday afternoon, the alpine rescue team and an air ambulance reached the man in a remote area. Attempts to revive him were unsuccessful. Etna, the highest active volcano in Europe, has seen a significant increase in activity over the past week.

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